Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.

@inproceedings{zoller2015publication, author = {Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas}, title = {On Publication Usage in a Social Bookmarking System}, booktitle = {Proceedings of the 2015 ACM Conference on Web Science}, year = {2015}, keywords = {2015, altmetrics, bookmarking, impact, myown, publication, social, usage}, abstract = {Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.} }

%0 = inproceedings %A = Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas %B = Proceedings of the 2015 ACM Conference on Web Science %D = 2015 %T = On Publication Usage in a Social Bookmarking System

The combination of ubiquitous and social computing is an emerging
esearch area which integrates different but complementary methods,
echniques and tools. In this paper, we focus on the Ubicon platform,
ts applications, and a large spectrum of analysis results.

bicon provides an extensible framework for building and hosting applications
argeting both ubiquitous and social environments. We summarize the
rchitecture and exemplify its implementation using four real-world
pplications built on top of Ubicon. In addition, we discuss several
cientific experiments in the context of these applications in order
o give a better picture of the potential of the framework, and discuss
nalysis results using several real-world data sets collected utilizing
bicon.

@article{atzmueller2014ubicon, author = {Atzmueller, Martin and Becker, Martin and Kibanov, Mark and Scholz, Christoph and Doerfel, Stephan and Hotho, Andreas and Macek, Bjoern-Elmar and Mitzlaff, Folke and Mueller, Juergen and Stumme, Gerd}, title = {Ubicon and its Applications for Ubiquitous Social Computing}, journal = {New Review of Hypermedia and Multimedia}, year = {2014}, volume = {20}, number = {1}, pages = {53--77}, url = {http://www.tandfonline.com/doi/abs/10.1080/13614568.2013.873488}, doi = {10.1080/13614568.2013.873488}, keywords = {2014, analytics, mining, myown, social, ubicon, ubiquitous}, abstract = {The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.} }

%0 = article %A = Atzmueller, Martin and Becker, Martin and Kibanov, Mark and Scholz, Christoph and Doerfel, Stephan and Hotho, Andreas and Macek, Bjoern-Elmar and Mitzlaff, Folke and Mueller, Juergen and Stumme, Gerd %D = 2014 %T = Ubicon and its Applications for Ubiquitous Social Computing %U = http://www.tandfonline.com/doi/abs/10.1080/13614568.2013.873488

@inproceedings{doerfel2014social, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, title = {How Social is Social Tagging?}, booktitle = {Proceedings of the 23rd International World Wide Web Conference}, series = {WWW 2014}, publisher = {ACM}, address = {New York, NY, USA}, year = {2014}, keywords = {2014, WWW, analyis, behavior, log, myown, social, tagging} }

%0 = inproceedings %A = Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus %B = Proceedings of the 23rd International World Wide Web Conference %C = New York, NY, USA %D = 2014 %I = ACM %T = How Social is Social Tagging?

Social tagging systems have established themselves as an important part in
day's web and have attracted the interest from our research community in a
riety of investigations. The overall vision of our community is that simply
rough interactions with the system, i.e., through tagging and sharing of
sources, users would contribute to building useful semantic structures as
ll as resource indexes using uncontrolled vocabulary not only due to the
sy-to-use mechanics. Henceforth, a variety of assumptions about social
gging systems have emerged, yet testing them has been difficult due to the
sence of suitable data. In this work we thoroughly investigate three
ailable assumptions - e.g., is a tagging system really social? - by examining
ve log data gathered from the real-world public social tagging system
bSonomy. Our empirical results indicate that while some of these assumptions
ld to a certain extent, other assumptions need to be reflected and viewed in
very critical light. Our observations have implications for the design of
ture search and other algorithms to better reflect the actual user behavior.

@misc{doerfel2014course, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, title = {Of course we share! Testing Assumptions about Social Tagging Systems}, year = {2014}, note = {cite arxiv:1401.0629}, url = {http://arxiv.org/abs/1401.0629}, keywords = {2014, myown, share, social, tagging}, abstract = {Social tagging systems have established themselves as an important part in
day's web and have attracted the interest from our research community in a
riety of investigations. The overall vision of our community is that simply
rough interactions with the system, i.e., through tagging and sharing of
sources, users would contribute to building useful semantic structures as
ll as resource indexes using uncontrolled vocabulary not only due to the
sy-to-use mechanics. Henceforth, a variety of assumptions about social
gging systems have emerged, yet testing them has been difficult due to the
sence of suitable data. In this work we thoroughly investigate three
ailable assumptions - e.g., is a tagging system really social? - by examining
ve log data gathered from the real-world public social tagging system
bSonomy. Our empirical results indicate that while some of these assumptions
ld to a certain extent, other assumptions need to be reflected and viewed in
very critical light. Our observations have implications for the design of
ture search and other algorithms to better reflect the actual user behavior.} }

%0 = misc %A = Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus %D = 2014 %T = Of course we share! Testing Assumptions about Social Tagging Systems %U = http://arxiv.org/abs/1401.0629

@proceedings{jannach2014proceedings,, title = {Proceedings of the 6th Workshop on Recommender Systems and the Social
Web (RSWeb 2014) co-located with the 8th ACM Conference on Recommender
Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014}, editor = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, series = {CEUR Workshop Proceedings}, publisher = {CEUR-WS.org}, year = {2014}, volume = {1271}, url = {http://ceur-ws.org/Vol-1271}, keywords = {2014, myown, proceedings, recommender, social, workshop} }

%0 = proceedings %D = 2014 %I = CEUR-WS.org %T = Proceedings of the 6th Workshop on Recommender Systems and the Social
Web (RSWeb 2014) co-located with the 8th ACM Conference on Recommender
Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014 %U = http://ceur-ws.org/Vol-1271

@inproceedings{jannach2014sixth, author = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, title = {The sixth ACM RecSys workshop on recommender systems and the social
web}, booktitle = {Eighth ACM Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, USA - October 06 - 10, 2014}, year = {2014}, pages = {395}, url = {http://doi.acm.org/10.1145/2645710.2645786}, doi = {10.1145/2645710.2645786}, keywords = {2014, introduction, myown, recommender, social, workshop} }

%0 = inproceedings %A = Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad %B = Eighth ACM Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, USA - October 06 - 10, 2014 %D = 2014 %T = The sixth ACM RecSys workshop on recommender systems and the social
web %U = http://doi.acm.org/10.1145/2645710.2645786

Applications of the Social Web are ubiquitous and have become an integral part of everyday life: Users make friends, for example, with the help of online social networks, share thoughts via Twitter, or collaboratively write articles in Wikipedia. All such interactions leave digital traces; thus, users participate in the creation of heterogeneous, distributed, collaborative data collections. In linguistics, the

@article{noKey, author = {Mitzlaff, Folke and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd}, title = {The social distributional hypothesis: a pragmatic proxy for homophily in online social networks}, journal = {Social Network Analysis and Mining}, publisher = {Springer Vienna}, year = {2014}, volume = {4}, number = {1}, url = {http://dx.doi.org/10.1007/s13278-014-0216-2}, doi = {10.1007/s13278-014-0216-2}, issn = {1869-5450}, keywords = {2014, distributional, hypothesis, myown, pragmatic, proxy, social}, abstract = {Applications of the Social Web are ubiquitous and have become an integral part of everyday life: Users make friends, for example, with the help of online social networks, share thoughts via Twitter, or collaboratively write articles in Wikipedia. All such interactions leave digital traces; thus, users participate in the creation of heterogeneous, distributed, collaborative data collections. In linguistics, the } }

%0 = article %A = Mitzlaff, Folke and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd %D = 2014 %I = Springer Vienna %T = The social distributional hypothesis: a pragmatic proxy for homophily in online social networks %U = http://dx.doi.org/10.1007/s13278-014-0216-2

When users interact with the Web today, they leave sequential digital trails
a massive scale. Examples of such human trails include Web navigation,
quences of online restaurant reviews, or online music play lists.
derstanding the factors that drive the production of these trails can be
eful for e.g., improving underlying network structures, predicting user
icks or enhancing recommendations. In this work, we present a general
proach called HypTrails for comparing a set of hypotheses about human trails
the Web, where hypotheses represent beliefs about transitions between
ates. Our approach utilizes Markov chain models with Bayesian inference. The
in idea is to incorporate hypotheses as informative Dirichlet priors and to
verage the sensitivity of Bayes factors on the prior for comparing hypotheses
th each other. For eliciting Dirichlet priors from hypotheses, we present an
aption of the so-called (trial) roulette method. We demonstrate the general
chanics and applicability of HypTrails by performing experiments with (i)
nthetic trails for which we control the mechanisms that have produced them
d (ii) empirical trails stemming from different domains including website
vigation, business reviews and online music played. Our work expands the
pertoire of methods available for studying human trails on the Web.

@misc{singer2014hyptrails, author = {Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus}, title = {HypTrails: A Bayesian Approach for Comparing Hypotheses about Human
Trails on the Web}, year = {2014}, note = {cite arxiv:1411.2844}, url = {http://arxiv.org/abs/1411.2844}, keywords = {2014, bayesian, comparing, hypotheses, myown, semantic, social}, abstract = {When users interact with the Web today, they leave sequential digital trails
a massive scale. Examples of such human trails include Web navigation,
quences of online restaurant reviews, or online music play lists.
derstanding the factors that drive the production of these trails can be
eful for e.g., improving underlying network structures, predicting user
icks or enhancing recommendations. In this work, we present a general
proach called HypTrails for comparing a set of hypotheses about human trails
the Web, where hypotheses represent beliefs about transitions between
ates. Our approach utilizes Markov chain models with Bayesian inference. The
in idea is to incorporate hypotheses as informative Dirichlet priors and to
verage the sensitivity of Bayes factors on the prior for comparing hypotheses
th each other. For eliciting Dirichlet priors from hypotheses, we present an
aption of the so-called (trial) roulette method. We demonstrate the general
chanics and applicability of HypTrails by performing experiments with (i)
nthetic trails for which we control the mechanisms that have produced them
d (ii) empirical trails stemming from different domains including website
vigation, business reviews and online music played. Our work expands the
pertoire of methods available for studying human trails on the Web.} }

%0 = misc %A = Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus %D = 2014 %T = HypTrails: A Bayesian Approach for Comparing Hypotheses about Human
Trails on the Web %U = http://arxiv.org/abs/1411.2844

@article{strohmaier2014computational, author = {Strohmaier, Markus and Wagner, Claudia}, title = {Computational Social Science for the World Wide Web}, journal = {Intelligent Systems}, publisher = {IEEE }, year = {2014}, pages = {84-88}, keywords = {computational, social} }

%0 = article %A = Strohmaier, Markus and Wagner, Claudia %D = 2014 %I = IEEE %T = Computational Social Science for the World Wide Web

@book{atzmueller2013ubiquitous,, title = {Ubiquitous Social Media Analysis Third International Workshops, MUSE 2012, Bristol, UK, September 24, 2012, and MSM 2012, Milwaukee, WI, USA, June 25, 2012, Revised Selected Papers}, editor = {Atzmueller, Martin and Chin, Alvin and Helic, Denis and Hotho, Andreas}, publisher = {Imprint: Springer}, address = {Berlin, Heidelberg}, year = {2013}, url = {http://link.springer.com/book/10.1007/978-3-642-45392-2}, isbn = {9783642453915 3642453910 9783642453922 3642453929}, keywords = {2013, analysis, bibsonomy, media, myown, postproceedings, social, workshop} }

%0 = book %C = Berlin, Heidelberg %D = 2013 %I = Imprint: Springer %T = Ubiquitous Social Media Analysis Third International Workshops, MUSE 2012, Bristol, UK, September 24, 2012, and MSM 2012, Milwaukee, WI, USA, June 25, 2012, Revised Selected Papers %U = http://link.springer.com/book/10.1007/978-3-642-45392-2

The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.

@article{landia2013deeper, author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan}, title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations}, journal = {cs.IR}, year = {2013}, volume = {1310.1498}, url = {http://arxiv.org/abs/1310.1498}, keywords = {2013, bookmarking, collaborative, folkrank, folksonomy, graph, myown, recommender, social, tagging}, abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.} }

%0 = article %A = Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan %D = 2013 %T = Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations %U = http://arxiv.org/abs/1310.1498

@incollection{MASH:13, author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas}, title = {Semantics of User Interaction in Social Media}, editor = {Ghoshal, Gourab and Poncela-Casasnovas, Julia and Tolksdorf, Robert}, booktitle = {Complex Networks IV}, series = {Studies in Computational Intelligence}, publisher = {Springer Verlag}, address = {Heidelberg, Germany}, year = {2013}, volume = {476}, doi = {10.1007/978-3-642-36844-8_2}, keywords = {2013, media, myown, semantic, social, user} }

%0 = incollection %A = Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas %B = Complex Networks IV %C = Heidelberg, Germany %D = 2013 %I = Springer Verlag %T = Semantics of User Interaction in Social Media

@article{journals/corr/MitzlaffABHS13, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {User-Relatedness and Community Structure in Social Interaction Networks.}, journal = {CoRR}, year = {2013}, volume = {abs/1309.3888}, url = {http://dblp.uni-trier.de/db/journals/corr/corr1309.html#MitzlaffABHS13}, keywords = {bibsonomy, relatedness, social, structure, user} }

%0 = article %A = Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %D = 2013 %T = User-Relatedness and Community Structure in Social Interaction Networks. %U = http://dblp.uni-trier.de/db/journals/corr/corr1309.html#MitzlaffABHS13

@proceedings{conf/recsys/2013rsweb,, title = {Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013.}, editor = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Freyne, Jill and Hotho, Andreas and Anand, Sarabjot Singh and Guy, Ido}, booktitle = {RSWeb@RecSys}, series = {CEUR Workshop Proceedings}, publisher = {CEUR-WS.org}, year = {2013}, volume = {1066}, url = {http://ceur-ws.org/Vol-1066}, keywords = {2013, bibsonomy, l3s, myown, recommender, social, web, workshop} }

%0 = proceedings %B = RSWeb@RecSys %D = 2013 %I = CEUR-WS.org %T = Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013. %U = http://ceur-ws.org/Vol-1066

@article{sun2013social, author = {Sun, Xiaoling and Kaur, Jasleen and Milojevic, Stasa and Flammini, Alessandro and Menczer, Filippo}, title = {Social Dynamics of Science}, journal = {Sci. Rep.}, publisher = {Macmillan Publishers Limited. All rights reserved}, year = {2013}, volume = {3}, url = {http://dx.doi.org/10.1038/srep01069}, keywords = {bibsonomy, dynamics, science, social, toread, web} }

%0 = article %A = Sun, Xiaoling and Kaur, Jasleen and Milojevic, Stasa and Flammini, Alessandro and Menczer, Filippo %D = 2013 %I = Macmillan Publishers Limited. All rights reserved %T = Social Dynamics of Science %U = http://dx.doi.org/10.1038/srep01069

@book{ACHH:12,, title = {Modeling and Mining Ubiquitous Social Media}, editor = {Atzmueller, Martin and Chin, Alvin and Helic, Denis and Hotho, Andreas}, series = {Lecture Notes in Computer Science}, publisher = {Springer Verlag}, address = {Heidelberg, Germany}, year = {2012}, volume = {7472}, url = {http://www.springer.com/computer/ai/book/978-3-642-33683-6}, keywords = {2012, everyaware, media, mining, modeling, myown, social, ubiquitous} }

%0 = book %C = Heidelberg, Germany %D = 2012 %I = Springer Verlag %T = Modeling and Mining Ubiquitous Social Media %U = http://www.springer.com/computer/ai/book/978-3-642-33683-6

The connection of ubiquitous and social computing is an emerging research area which is combining two prominent areas of computer science. In this paper, we tackle this topic from different angles: We describe data mining methods for ubiquitous and social data, specifically focusing on physical and social activities, and provide exemplary analysis results. Furthermore, we give an overview on the Ubicon platform which provides a framework for the creation and hosting of ubiquitous and social applications for diverse tasks and projects. Ubicon features the collection and analysis of both physical and social activities of users for enabling inter-connected applications in ubiquitous and social contexts. We summarize three real-world systems built on top of Ubicon, and exemplarily discuss the according mining and analysis aspects.

@inproceedings{mueller-2012, author = {Atzmueller, Martin and Becker, Martin and Doerfel, Stephan and Kibanov, Mark and Hotho, Andreas and Macek, Björn-Elmar and Mitzlaff, Folke and Mueller, Juergen and Scholz, Christoph and Stumme, Gerd}, title = {Ubicon: Observing Social and Physical Activities}, booktitle = {IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012, Besançon, France, 20-23 November, 2012}, publisher = {IEEE}, address = {Washington, DC, USA}, year = {2012}, keywords = {2012, activities, everyaware, myown, observing, physical, social, ubicon}, abstract = {The connection of ubiquitous and social computing is an emerging research area which is combining two prominent areas of computer science. In this paper, we tackle this topic from different angles: We describe data mining methods for ubiquitous and social data, specifically focusing on physical and social activities, and provide exemplary analysis results. Furthermore, we give an overview on the Ubicon platform which provides a framework for the creation and hosting of ubiquitous and social applications for diverse tasks and projects. Ubicon features the collection and analysis of both physical and social activities of users for enabling inter-connected applications in ubiquitous and social contexts. We summarize three real-world systems built on top of Ubicon, and exemplarily discuss the according mining and analysis aspects.} }

%0 = inproceedings %A = Atzmueller, Martin and Becker, Martin and Doerfel, Stephan and Kibanov, Mark and Hotho, Andreas and Macek, Björn-Elmar and Mitzlaff, Folke and Mueller, Juergen and Scholz, Christoph and Stumme, Gerd %B = IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012, Besançon, France, 20-23 November, 2012 %C = Washington, DC, USA %D = 2012 %I = IEEE %T = Ubicon: Observing Social and Physical Activities

Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.

@book{balbymarinho2012recommender, author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.}, title = {Recommender Systems for Social Tagging Systems}, series = {SpringerBriefs in Electrical and Computer Engineering}, publisher = {Springer}, year = {2012}, url = {http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1}, isbn = {978-1-4614-1893-1}, keywords = {2012, bookmarking, collaborative, folksonomy, myown, recommender, social, tagging}, abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.} }

%0 = book %A = Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P. %D = 2012 %I = Springer %T = Recommender Systems for Social Tagging Systems %U = http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1

The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.

@inproceedings{Doerfel:2012:LPM:2365934.2365937, author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, title = {Leveraging publication metadata and social data into FolkRank for scientific publication recommendation}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, series = {RSWeb '12}, publisher = {ACM}, address = {New York, NY, USA}, year = {2012}, pages = {9--16}, url = {http://doi.acm.org/10.1145/2365934.2365937}, doi = {10.1145/2365934.2365937}, isbn = {978-1-4503-1638-5}, keywords = {2012, bookmarking, folkrank, myown, recommender, social, tagging}, abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.} }

%0 = inproceedings %A = Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web %C = New York, NY, USA %D = 2012 %I = ACM %T = Leveraging publication metadata and social data into FolkRank for scientific publication recommendation %U = http://doi.acm.org/10.1145/2365934.2365937

Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.

@incollection{jaeschke2012challenges, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, booktitle = {Recommender Systems for the Social Web}, series = {Intelligent Systems Reference Library}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2012}, volume = {32}, pages = {65--87}, url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}, doi = {10.1007/978-3-642-25694-3_3}, isbn = {978-3-642-25694-3}, keywords = {2012, bookmarking, challenge, collaborative, dc09, discovery, folksonomy, myown, recommender, rsdc08, social, tagging}, abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.} }

%0 = incollection %A = Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd %B = Recommender Systems for the Social Web %C = Berlin/Heidelberg %D = 2012 %I = Springer %T = Challenges in Tag Recommendations for Collaborative Tagging Systems %U = http://dx.doi.org/10.1007/978-3-642-25694-3_3

Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.</p> <p>Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.

@inproceedings{Landia:2012:EFC:2365934.2365936, author = {Landia, Nikolas and Anand, Sarabjot Singh and Hotho, Andreas and Jäschke, Robert and Doerfel, Stephan and Mitzlaff, Folke}, title = {Extending FolkRank with content data}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, series = {RSWeb '12}, publisher = {ACM}, address = {New York, NY, USA}, year = {2012}, pages = {1--8}, url = {http://doi.acm.org/10.1145/2365934.2365936}, doi = {10.1145/2365934.2365936}, isbn = {978-1-4503-1638-5}, keywords = {2012, bookmarking, folkrank, folksonomy, myown, social, tagging}, abstract = {Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.</p> <p>Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.} }

%0 = inproceedings %A = Landia, Nikolas and Anand, Sarabjot Singh and Hotho, Andreas and Jäschke, Robert and Doerfel, Stephan and Mitzlaff, Folke %B = Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web %C = New York, NY, USA %D = 2012 %I = ACM %T = Extending FolkRank with content data %U = http://doi.acm.org/10.1145/2365934.2365936

The new opportunities for applying recommendation techniques within Social Web platforms and applications as well as the various new sources of information which have become available in the Web 2.0 and can be incorporated in future recommender applications are a strong driving factor in current recommender system research for various reasons:</p> <p>(1) Social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation.</p> <p>(2) New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders cannot only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development.</p> <p>(3) Recommender technology can assist Social Web systems through increasing adoption and participation and sustaining membership. Through targeted and timely intervention which stimulates traffic and interaction, recommender technology can play its role in sustaining the success of the Social Web.</p> <p>(4) The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people and can require more interactive and richer recommender systems user interfaces.</p> <p>The technical papers appearing in these proceedings aim to explore and understand challenges and new opportunities for recommender systems in the Social Web and were selected in a formal review process by an international program committee.</p> <p>Overall, we received 13 paper submissions from 12 different countries, out of which 7 long papers and 1 short paper were selected for presentation and inclusion in the proceedings. The submitted papers addressed a variety of topics related to Social Web recommender systems from the use of microblogging data for personalization over new tag recommendation approaches to social media-based personalization of news.

@proceedings{Mobasher:2012:2365934, author = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Hotho, Andreas}, title = {RSWeb '12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, publisher = {ACM}, address = {New York, NY, USA}, year = {2012}, note = {609126}, isbn = {978-1-4503-1638-5}, keywords = {2012, acm, myown, recommender, rsweb, social, web, workshop}, abstract = {The new opportunities for applying recommendation techniques within Social Web platforms and applications as well as the various new sources of information which have become available in the Web 2.0 and can be incorporated in future recommender applications are a strong driving factor in current recommender system research for various reasons:</p> <p>(1) Social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation.</p> <p>(2) New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders cannot only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development.</p> <p>(3) Recommender technology can assist Social Web systems through increasing adoption and participation and sustaining membership. Through targeted and timely intervention which stimulates traffic and interaction, recommender technology can play its role in sustaining the success of the Social Web.</p> <p>(4) The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people and can require more interactive and richer recommender systems user interfaces.</p> <p>The technical papers appearing in these proceedings aim to explore and understand challenges and new opportunities for recommender systems in the Social Web and were selected in a formal review process by an international program committee.</p> <p>Overall, we received 13 paper submissions from 12 different countries, out of which 7 long papers and 1 short paper were selected for presentation and inclusion in the proceedings. The submitted papers addressed a variety of topics related to Social Web recommender systems from the use of microblogging data for personalization over new tag recommendation approaches to social media-based personalization of news.} }

%0 = proceedings %A = Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Hotho, Andreas %C = New York, NY, USA %D = 2012 %I = ACM %T = RSWeb '12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web

@proceedings{conf/ht/2010msmmuse,, title = {Analysis of Social Media and Ubiquitous Data - International Workshops MSM 2010, Toronto, Canada, June 13, 2010, and MUSE 2010, Barcelona, Spain, September 20, 2010, Revised Selected Papers}, editor = {Atzmueller, Martin and Hotho, Andreas and Strohmaier, Markus and Chin, Alvin}, booktitle = {MSM/MUSE}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, year = {2011}, volume = {6904}, url = {http://dblp.uni-trier.de/db/conf/ht/msmmuse2010.html}, isbn = {978-3-642-23598-6}, keywords = {2011, analysis, data, media, myown, social, ubiquitous} }

%0 = proceedings %B = MSM/MUSE %D = 2011 %I = Springer %T = Analysis of Social Media and Ubiquitous Data - International Workshops MSM 2010, Toronto, Canada, June 13, 2010, and MUSE 2010, Barcelona, Spain, September 20, 2010, Revised Selected Papers %U = http://dblp.uni-trier.de/db/conf/ht/msmmuse2010.html

@article{martin2011enhancing, author = {Atzmueller, Martin and Benz, Dominik and Doerfel, Stephan and Hotho, Andreas and Jäschke, Robert and Macek, Bjoern Elmar and Mitzlaff, Folke and Scholz, Christoph and Stumme, Gerd}, title = {Enhancing Social Interactions at Conferences}, booktitle = {it - Information Technology}, journal = {it - Information Technology}, publisher = {Oldenbourg Wissenschaftsverlag GmbH}, year = {2011}, volume = {53}, number = {3}, pages = {101--107}, url = {http://dx.doi.org/10.1524/itit.2011.0631}, doi = {10.1524/itit.2011.0631}, issn = {16112776}, keywords = {2011, conferator, conference, enhancing, journal, myown, social} }

%0 = article %A = Atzmueller, Martin and Benz, Dominik and Doerfel, Stephan and Hotho, Andreas and Jäschke, Robert and Macek, Bjoern Elmar and Mitzlaff, Folke and Scholz, Christoph and Stumme, Gerd %B = it - Information Technology %D = 2011 %I = Oldenbourg Wissenschaftsverlag GmbH %T = Enhancing Social Interactions at Conferences %U = http://dx.doi.org/10.1524/itit.2011.0631

@inproceedings{atzmueller2011towards, author = {Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Towards Mining Semantic Maturity in Social Bookmarking Systems}, editor = {Passant, Alexandre and Fernández, Sergio and Breslin, John and Bojārs, Uldis}, booktitle = {Proceedings of the 4th international workshop on Social Data on the Web (SDoW2011)}, year = {2011}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/atzmueller2011towards.pdf}, keywords = {2011, bookmarking, mining, myown, pattern, semantic, social} }

%0 = inproceedings %A = Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the 4th international workshop on Social Data on the Web (SDoW2011) %D = 2011 %T = Towards Mining Semantic Maturity in Social Bookmarking Systems %U = http://www.kde.cs.uni-kassel.de/pub/pdf/atzmueller2011towards.pdf

Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.

@inproceedings{benz2011measuring, author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus}, title = {One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata}, editor = {Antoniou, Grigoris and Grobelnik, Marko and Simperl, Elena and Parsia, Bijan and Plexousakis, Dimitris and Pan, Jeff and Leenheer, Pieter De}, booktitle = {Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011)}, address = {Heraklion, Crete}, year = {2011}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf}, keywords = {2011, abstractness, measuring, myown, social, term}, abstract = {Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.} }

%0 = inproceedings %A = Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus %B = Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011) %C = Heraklion, Crete %D = 2011 %T = One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata %U = http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf

@inproceedings{benz2011measuring, author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus}, title = {One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata}, booktitle = {Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation}, year = {2011}, keywords = {2011, abstractness, measuring, metadata, myown, social} }

%0 = inproceedings %A = Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus %B = Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation %D = 2011 %T = One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata

@inproceedings{bullock2011tagging, author = {Bullock, Beate Navarro and Jäschke, Robert and Hotho, Andreas}, title = {Tagging data as implicit feedback for learning-to-rank}, booktitle = {Proceedings of the ACM WebSci'11}, year = {2011}, url = {http://journal.webscience.org/463/}, keywords = {2011, dm, feedback, learning, logsonomy, ml, myown, search, social} }

%0 = inproceedings %A = Bullock, Beate Navarro and Jäschke, Robert and Hotho, Andreas %B = Proceedings of the ACM WebSci'11 %D = 2011 %T = Tagging data as implicit feedback for learning-to-rank %U = http://journal.webscience.org/463/

Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.

@article{burke2011recommendation, author = {Burke, Robin and Gemmell, Jonathan and Hotho, Andreas and Jäschke, Robert}, title = {Recommendation in the Social Web}, journal = {AI Magazine}, publisher = {Association for the Advancement of Artificial Intelligence}, year = {2011}, volume = {32}, number = {3}, pages = {46--56}, url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2373}, keywords = {2011, collaborative, myown, recommender, social, tagging, taggingsurvey, web}, abstract = {Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.} }

%0 = article %A = Burke, Robin and Gemmell, Jonathan and Hotho, Andreas and Jäschke, Robert %D = 2011 %I = Association for the Advancement of Artificial Intelligence %T = Recommendation in the Social Web %U = http://www.aaai.org/ojs/index.php/aimagazine/article/view/2373

Social tagging recommendation is an urgent and useful enabling technology for Web 2.0. In this paper, we present a systematic study of low-order tensor decomposition approach that are specifically targeted at the very sparse data problem in tagging recommendation problem. Low-order polynomials have low functional complexity, are uniquely capable of enhancing statistics and also avoids over-fitting than traditional tensor decompositions such as Tucker and Parafac decompositions. We perform extensive experiments on several datasets and compared with 6 existing methods. Experimental results demonstrate that our approach outperforms existing approaches.

@inproceedings{Cai:2011:LTD:1935826.1935920, author = {Cai, Yuanzhe and Zhang, Miao and Luo, Dijun and Ding, Chris and Chakravarthy, Sharma}, title = {Low-order tensor decompositions for social tagging recommendation}, booktitle = {Proceedings of the fourth ACM international conference on Web search and data mining}, series = {WSDM '11}, publisher = {ACM}, address = {New York, NY, USA}, year = {2011}, pages = {695--704}, url = {http://doi.acm.org/10.1145/1935826.1935920}, doi = {10.1145/1935826.1935920}, isbn = {978-1-4503-0493-1}, keywords = {decomposion, recommender, social, tagging, taggingsurvey, tensor}, abstract = {Social tagging recommendation is an urgent and useful enabling technology for Web 2.0. In this paper, we present a systematic study of low-order tensor decomposition approach that are specifically targeted at the very sparse data problem in tagging recommendation problem. Low-order polynomials have low functional complexity, are uniquely capable of enhancing statistics and also avoids over-fitting than traditional tensor decompositions such as Tucker and Parafac decompositions. We perform extensive experiments on several datasets and compared with 6 existing methods. Experimental results demonstrate that our approach outperforms existing approaches.} }

%0 = inproceedings %A = Cai, Yuanzhe and Zhang, Miao and Luo, Dijun and Ding, Chris and Chakravarthy, Sharma %B = Proceedings of the fourth ACM international conference on Web search and data mining %C = New York, NY, USA %D = 2011 %I = ACM %T = Low-order tensor decompositions for social tagging recommendation %U = http://doi.acm.org/10.1145/1935826.1935920

@article{doi:10.1080/13614568.2011.641407, author = {Cattuto, Ciro and Hotho, Andreas}, title = {Introduction to the Special Issue on Social Linking and Hypermedia}, journal = {New Review of Hypermedia and Multimedia}, year = {2011}, volume = {17}, number = {3}, pages = {241-242}, url = {http://www.tandfonline.com/doi/abs/10.1080/13614568.2011.641407}, doi = {10.1080/13614568.2011.641407}, keywords = {2011, issue, linking, myown, social, special} }

%0 = article %A = Cattuto, Ciro and Hotho, Andreas %D = 2011 %T = Introduction to the Special Issue on Social Linking and Hypermedia %U = http://www.tandfonline.com/doi/abs/10.1080/13614568.2011.641407

The exponential growth of the social web poses challenges and new opportunities for recommender systems. The social web has turned information consumers into active contributors creating massive amounts of information. Finding relevant and interesting content at the right time and in the right context is challenging for existing recommender approaches. At the same time, social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. The Social Web provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.</p> <p>The goal of this one day workshop was to bring together researchers and practitioners to explore, discuss, and understand challenges and new opportunities for Recommender Systems and the Social Web. The workshop consisted both of technical sessions, in which selected participants presented their results or ongoing research, as well as informal breakout sessions on more focused topics.</p> <p>Papers discussing various aspects of recommender system in the Social Web were submitted and selected for presentation and discussion in the workshop in a formal reviewing process: Case studies and novel fielded social recommender applications; Economy of community-based systems: Using recommenders to encourage users to contribute and sustain participation.; Social network and folksonomy development: Recommending friends, tags, bookmarks, blogs, music, communities etc.; Recommender systems mash-ups, Web 2.0 user interfaces, rich media recommender systems; Collaborative knowledge authoring, collective intelligence; Recommender applications involving users or groups directly in the recommendation process; Exploiting folksonomies, social network information, interaction, user context and communities or groups for recommendations; Trust and reputation aware social recommendations; Semantic Web recommender systems, use of ontologies or microformats; Empirical evaluation of social recommender techniques, success and failure measures</p> <p>Full workshop details are available at http://www.dcs.warwick.ac.uk/~ssanand/RSWeb11/index.htm

@inproceedings{Freyne:2011:WRS:2043932.2044014, author = {Freyne, Jill and Anand, Sarabjot Singh and Guy, Ido and Hotho, Andreas}, title = {3rd workshop on recommender systems and the social web}, booktitle = {Proceedings of the fifth ACM conference on Recommender systems}, series = {RecSys '11}, publisher = {ACM}, address = {New York, NY, USA}, year = {2011}, pages = {383--384}, url = {http://doi.acm.org/10.1145/2043932.2044014}, doi = {10.1145/2043932.2044014}, isbn = {978-1-4503-0683-6}, keywords = {2011, cochair, myown, recommender, social, systems, workshop}, abstract = {The exponential growth of the social web poses challenges and new opportunities for recommender systems. The social web has turned information consumers into active contributors creating massive amounts of information. Finding relevant and interesting content at the right time and in the right context is challenging for existing recommender approaches. At the same time, social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. The Social Web provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.</p> <p>The goal of this one day workshop was to bring together researchers and practitioners to explore, discuss, and understand challenges and new opportunities for Recommender Systems and the Social Web. The workshop consisted both of technical sessions, in which selected participants presented their results or ongoing research, as well as informal breakout sessions on more focused topics.</p> <p>Papers discussing various aspects of recommender system in the Social Web were submitted and selected for presentation and discussion in the workshop in a formal reviewing process: Case studies and novel fielded social recommender applications; Economy of community-based systems: Using recommenders to encourage users to contribute and sustain participation.; Social network and folksonomy development: Recommending friends, tags, bookmarks, blogs, music, communities etc.; Recommender systems mash-ups, Web 2.0 user interfaces, rich media recommender systems; Collaborative knowledge authoring, collective intelligence; Recommender applications involving users or groups directly in the recommendation process; Exploiting folksonomies, social network information, interaction, user context and communities or groups for recommendations; Trust and reputation aware social recommendations; Semantic Web recommender systems, use of ontologies or microformats; Empirical evaluation of social recommender techniques, success and failure measures</p> <p>Full workshop details are available at http://www.dcs.warwick.ac.uk/~ssanand/RSWeb11/index.htm} }

%0 = inproceedings %A = Freyne, Jill and Anand, Sarabjot Singh and Guy, Ido and Hotho, Andreas %B = Proceedings of the fifth ACM conference on Recommender systems %C = New York, NY, USA %D = 2011 %I = ACM %T = 3rd workshop on recommender systems and the social web %U = http://doi.acm.org/10.1145/2043932.2044014

Web 2.0 technologies are finding their way into academics: specialized social bookmarking services allow researchers to store and share scientific literature online. By bookmarking and tagging articles, academic prosumers generate new information about resources, i.e. usage statistics and content description of scientific journals. Given the lack of global download statistics, the authors propose the application of social bookmarking data to journal evaluation. For a set of 45 physics journals all 13,608 bookmarks from CiteULike, Connotea and BibSonomy to documents published between 2004 and 2008 were analyzed. This article explores bookmarking data in STM and examines in how far it can be used to describe the perception of periodicals by the readership. Four basic indicators are defined, which analyze different aspects of usage: Usage Ratio, Usage Diffusion, Article Usage Intensity and Journal Usage Intensity. Tags are analyzed to describe a reader-specific view on journal content.

@article{Haustein2011446, author = {Haustein, Stefanie and Siebenlist, Tobias}, title = {Applying social bookmarking data to evaluate journal usage}, journal = {Journal of Informetrics}, year = {2011}, volume = {5}, number = {3}, pages = {446 - 457}, url = {http://www.sciencedirect.com/science/article/pii/S1751157711000393}, doi = {10.1016/j.joi.2011.04.002}, issn = {1751-1577}, keywords = {bookmarking, citation, journal, social, toread, usage}, abstract = {Web 2.0 technologies are finding their way into academics: specialized social bookmarking services allow researchers to store and share scientific literature online. By bookmarking and tagging articles, academic prosumers generate new information about resources, i.e. usage statistics and content description of scientific journals. Given the lack of global download statistics, the authors propose the application of social bookmarking data to journal evaluation. For a set of 45 physics journals all 13,608 bookmarks from CiteULike, Connotea and BibSonomy to documents published between 2004 and 2008 were analyzed. This article explores bookmarking data in STM and examines in how far it can be used to describe the perception of periodicals by the readership. Four basic indicators are defined, which analyze different aspects of usage: Usage Ratio, Usage Diffusion, Article Usage Intensity and Journal Usage Intensity. Tags are analyzed to describe a reader-specific view on journal content.} }

%0 = article %A = Haustein, Stefanie and Siebenlist, Tobias %D = 2011 %T = Applying social bookmarking data to evaluate journal usage %U = http://www.sciencedirect.com/science/article/pii/S1751157711000393

@inproceedings{conf/birthday/HothoS11, author = {Hotho, Andreas and Stumme, Gerd}, title = {From Semantic Web Mining to Social and Ubiquitous Mining - A Subjective View on Past, Current, and Future Research.}, editor = {Fensel, Dieter}, booktitle = {Foundations for the Web of Information and Services}, publisher = {Springer}, year = {2011}, pages = {143-153}, url = {http://dblp.uni-trier.de/db/conf/birthday/studer2011.html#HothoS11}, isbn = {978-3-642-19796-3}, keywords = {2011, mining, myown, social, ubiquitous, web} }

%0 = inproceedings %A = Hotho, Andreas and Stumme, Gerd %B = Foundations for the Web of Information and Services %D = 2011 %I = Springer %T = From Semantic Web Mining to Social and Ubiquitous Mining - A Subjective View on Past, Current, and Future Research. %U = http://dblp.uni-trier.de/db/conf/birthday/studer2011.html#HothoS11

@phdthesis{jschke2011formal, author = {Jäschke, Robert}, title = {Formal concept analysis and tag recommendations in collaborative tagging systems}, publisher = {IOS Press}, address = {[Amsterdam]}, year = {2011}, pages = {--}, url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=9783898383325}, isbn = {9781607507079 1607507072 9783898383325 3898383326}, keywords = {bibsonomy, bookmarking, dissertation, fca, recommender, social, tag, tagging, taggingsurvey} }

%0 = phdthesis %A = Jäschke, Robert %C = [Amsterdam] %D = 2011 %I = IOS Press %T = Formal concept analysis and tag recommendations in collaborative tagging systems %U = http://www.worldcat.org/search?qt=worldcat_org_all&q=9783898383325

@inproceedings{MASH:13b, author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas}, title = {On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission)}, booktitle = {Proc. LWA 2013 (KDML Special Track)}, publisher = {University of Bamberg}, address = {Bamberg, Germany}, year = {2011}, keywords = {2013, mining, myown, social, ubiquitous} }

%0 = inproceedings %A = Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas %B = Proc. LWA 2013 (KDML Special Track) %C = Bamberg, Germany %D = 2011 %I = University of Bamberg %T = On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission)

In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.

@article{pham2011development, author = {Pham, Manh and Klamma, Ralf and Jarke, Matthias}, title = {Development of computer science disciplines: a social network analysis approach}, journal = {Social Network Analysis and Mining}, publisher = {Springer}, address = {Wien}, year = {2011}, volume = {1}, number = {4}, pages = {321--340}, url = {http://dx.doi.org/10.1007/s13278-011-0024-x}, doi = {10.1007/s13278-011-0024-x}, issn = {1869-5450}, keywords = {analysis, computer, network, paper, scholary, science, scientometrics, sna, social}, abstract = {In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.} }

%0 = article %A = Pham, Manh and Klamma, Ralf and Jarke, Matthias %C = Wien %D = 2011 %I = Springer %T = Development of computer science disciplines: a social network analysis approach %U = http://dx.doi.org/10.1007/s13278-011-0024-x

@book{noauthororeditor2011privacy,, title = {Privacy Online Perspectives on Privacy and Self-disclosure in the Social Web.}, editor = {Trepte, Sabine and Reinecke, Leonard}, publisher = {Springer-Verlag New York Inc}, year = {2011}, pages = {--}, url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=9783642215209}, isbn = {9783642215209 3642215203}, keywords = {online, privacy, social, toread, web} }

%0 = book %D = 2011 %I = Springer-Verlag New York Inc %T = Privacy Online Perspectives on Privacy and Self-disclosure in the Social Web. %U = http://www.worldcat.org/search?qt=worldcat_org_all&q=9783642215209

We study the structure of the social graph of active Facebook users, the
rgest social network ever analyzed. We compute numerous features of the graph
cluding the number of users and friendships, the degree distribution, path
ngths, clustering, and mixing patterns. Our results center around three main
servations. First, we characterize the global structure of the graph,
termining that the social network is nearly fully connected, with 99.91% of
dividuals belonging to a single large connected component, and we confirm the
ix degrees of separation" phenomenon on a global scale. Second, by studying
e average local clustering coefficient and degeneracy of graph neighborhoods,
show that while the Facebook graph as a whole is clearly sparse, the graph
ighborhoods of users contain surprisingly dense structure. Third, we
aracterize the assortativity patterns present in the graph by studying the
sic demographic and network properties of users. We observe clear degree
sortativity and characterize the extent to which "your friends have more
iends than you". Furthermore, we observe a strong effect of age on friendship
eferences as well as a globally modular community structure driven by
tionality, but we do not find any strong gender homophily. We compare our
sults with those from smaller social networks and find mostly, but not
tirely, agreement on common structural network characteristics.

@misc{ugander2011anatomy, author = {Ugander, Johan and Karrer, Brian and Backstrom, Lars and Marlow, Cameron}, title = {The Anatomy of the Facebook Social Graph}, year = {2011}, note = {cite arxiv:1111.4503Comment: 17 pages, 9 figures, 1 table}, url = {http://arxiv.org/abs/1111.4503}, keywords = {facebook, graph, network, social}, abstract = {We study the structure of the social graph of active Facebook users, the
rgest social network ever analyzed. We compute numerous features of the graph
cluding the number of users and friendships, the degree distribution, path
ngths, clustering, and mixing patterns. Our results center around three main
servations. First, we characterize the global structure of the graph,
termining that the social network is nearly fully connected, with 99.91% of
dividuals belonging to a single large connected component, and we confirm the
ix degrees of separation" phenomenon on a global scale. Second, by studying
e average local clustering coefficient and degeneracy of graph neighborhoods,
show that while the Facebook graph as a whole is clearly sparse, the graph
ighborhoods of users contain surprisingly dense structure. Third, we
aracterize the assortativity patterns present in the graph by studying the
sic demographic and network properties of users. We observe clear degree
sortativity and characterize the extent to which "your friends have more
iends than you". Furthermore, we observe a strong effect of age on friendship
eferences as well as a globally modular community structure driven by
tionality, but we do not find any strong gender homophily. We compare our
sults with those from smaller social networks and find mostly, but not
tirely, agreement on common structural network characteristics.} }

%0 = misc %A = Ugander, Johan and Karrer, Brian and Backstrom, Lars and Marlow, Cameron %D = 2011 %T = The Anatomy of the Facebook Social Graph %U = http://arxiv.org/abs/1111.4503

@book{AH:10,, title = {Proceedings of the 2010 Workshop on Mining Ubiquitous and Social Environments (MUSE 2010)}, editor = {Atzmueller, Martin and Hotho, Andreas}, publisher = {ECML/PKDD 2010}, address = {Barcelona, Spain}, year = {2010}, keywords = {2010, muse, myown, proceedings, social, ubiquitous, workshop} }

%0 = book %C = Barcelona, Spain %D = 2010 %I = ECML/PKDD 2010 %T = Proceedings of the 2010 Workshop on Mining Ubiquitous and Social Environments (MUSE 2010)

Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.

@article{benz2010social, author = {Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Mitzlaff, Folke and Schmitz, Christoph and Stumme, Gerd}, title = {The Social Bookmark and Publication Management System BibSonomy}, journal = {The VLDB Journal}, publisher = {Springer}, address = {Berlin / Heidelberg}, year = {2010}, volume = {19}, number = {6}, pages = {849--875}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/benz2010social.pdf}, doi = {10.1007/s00778-010-0208-4}, issn = {1066-8888}, keywords = {2010, bibsonomy, myown, publication, social, system, tagging, taggingsurvey, vldb}, abstract = {Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.} }

%0 = article %A = Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Mitzlaff, Folke and Schmitz, Christoph and Stumme, Gerd %C = Berlin / Heidelberg %D = 2010 %I = Springer %T = The Social Bookmark and Publication Management System BibSonomy %U = http://www.kde.cs.uni-kassel.de/pub/pdf/benz2010social.pdf

@article{Berendt201095, author = {Berendt, Bettina and Hotho, Andreas and Stumme, Gerd}, title = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, year = {2010}, volume = {8}, number = {2-3}, pages = {95 - 96}, note = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences}, url = {http://www.sciencedirect.com/science/article/B758F-4YXK4HW-1/2/4cb514565477c54160b5e6eb716c32d7}, doi = {DOI: 10.1016/j.websem.2010.04.008}, issn = {1570-8268}, keywords = {2010, data, introduction, mining, myown, network, semantic, social, web} }

%0 = article %A = Berendt, Bettina and Hotho, Andreas and Stumme, Gerd %D = 2010 %T = Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0 %U = http://www.sciencedirect.com/science/article/B758F-4YXK4HW-1/2/4cb514565477c54160b5e6eb716c32d7

Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer größerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenwärtigkeit, die ständige Verfügbarkeit, aber auch die Möglichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gründe für ihren gegenwärtigen Erfolg. Der Artikel führt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabläufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbezügen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.

@article{hotho2010publikationsmanagement, author = {Hotho, Andreas and Benz, Dominik and Eisterlehner, Folke and Jäschke, Robert and Krause, Beate and Schmitz, Christoph and Stumme, Gerd}, title = {Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System für Wissenschaftler}, journal = {HMD -- Praxis der Wirtschaftsinformatik}, year = {2010}, volume = {Heft 271}, pages = {47-58}, issn = {1436-3011}, keywords = {2.0, 2010, bibsonomy, bookmarking, myown, social, tagging, taggingsurvey, web}, abstract = {Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer größerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenwärtigkeit, die ständige Verfügbarkeit, aber auch die Möglichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gründe für ihren gegenwärtigen Erfolg. Der Artikel führt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabläufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbezügen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.} }

%0 = article %A = Hotho, Andreas and Benz, Dominik and Eisterlehner, Folke and Jäschke, Robert and Krause, Beate and Schmitz, Christoph and Stumme, Gerd %D = 2010 %T = Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System für Wissenschaftler

@article{hotho2010ubiquitous, author = {Hotho, Andreas and Ulslev Pedersen, Rasmus and Wurst, Michael}, title = {Ubiquitous Data}, journal = {Lecture Notes in Computer Science}, publisher = {Springer}, year = {2010}, number = {6202}, pages = {61--74}, url = {http://rd.springer.com/content/pdf/10.1007%2F978-3-642-16392-0_4.pdf}, issn = {0302-9743}, keywords = {2010, data, dm, mining, myown, social, ubiquitous} }

%0 = article %A = Hotho, Andreas and Ulslev Pedersen, Rasmus and Wurst, Michael %D = 2010 %I = Springer %T = Ubiquitous Data %U = http://rd.springer.com/content/pdf/10.1007%2F978-3-642-16392-0_4.pdf

@article{lerch2010datenschutz, author = {Lerch, Hana and Krause, Beate and Hotho, Andreas and Roßnagel, Alexander and Stumme, Gerd}, title = {Social Bookmarking-Systeme – die unerkannten Datensammler - Ungewollte personenbezogene Datenverabeitung?}, journal = {MultiMedia und Recht}, year = {2010}, volume = {7}, pages = {454-458}, keywords = {2010, bibsonomy, bookmarking, datenschutz, info2.0, myown, social} }

%0 = article %A = Lerch, Hana and Krause, Beate and Hotho, Andreas and Roßnagel, Alexander and Stumme, Gerd %D = 2010 %T = Social Bookmarking-Systeme – die unerkannten Datensammler - Ungewollte personenbezogene Datenverabeitung?

Social tagging systems pose new challenges to developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of social tagging systems. In joining this stream of research, we have developed and evaluated two enhancements of user-based collaborative filtering algorithms to provide recommendations of articles on Cite ULike, a social tagging service for scientific articles. The result obtained after two phases of evaluation suggests that both enhancements are beneficial. Incorporating the number of raters into the algorithms, as we do in our NwCF approach, leads to an improvement of precision, while tag-based BM25 similarity measure, an alternative to Pearson correlation for calculating the similarity between users and their neighbors, increases the coverage of the recommendation process.

@article{citeulike:8506476, author = {Santander, Denis P. and Brusilovsky, Peter}, title = {Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles}, journal = {Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, year = {2010}, volume = {1}, pages = {136--142}, url = {http://dx.doi.org/10.1109/WI-IAT.2010.261}, doi = {10.1109/WI-IAT.2010.261}, isbn = {978-0-7695-4191-4}, keywords = {collaborative, recommender, social, tagging, taggingsurvey, toread}, abstract = {Social tagging systems pose new challenges to developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of social tagging systems. In joining this stream of research, we have developed and evaluated two enhancements of user-based collaborative filtering algorithms to provide recommendations of articles on Cite ULike, a social tagging service for scientific articles. The result obtained after two phases of evaluation suggests that both enhancements are beneficial. Incorporating the number of raters into the algorithms, as we do in our NwCF approach, leads to an improvement of precision, while tag-based BM25 similarity measure, an alternative to Pearson correlation for calculating the similarity between users and their neighbors, increases the coverage of the recommendation process.} }

%0 = article %A = Santander, Denis P. and Brusilovsky, Peter %C = Los Alamitos, CA, USA %D = 2010 %I = IEEE Computer Society %T = Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles %U = http://dx.doi.org/10.1109/WI-IAT.2010.261

The past decade has witnessed the emergence of participatory Web and social media, bringing people together in many creative ways. Millions of users are playing, tagging, working, and socializing online, demonstrating new forms of collaboration, communication, and intelligence that were hardly imaginable just a short time ago. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale. This lecture, from a data mining perspective, introduces characteristics of social media, reviews representative tasks of computing with social media, and illustrates associated challenges. It introduces basic concepts, presents state-of-the-art algorithms with easy-to-understand examples, and recommends effective evaluation methods. In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media. We also demonstrate how discovered patterns of communities can be used for social media mining. The concepts, algorithms, and methods presented in this lecture can help harness the power of social media and support building socially-intelligent systems. This book is an accessible introduction to the study of community detection and mining in social media. It is an essential reading for students, researchers, and practitioners in disciplines and applications where social media is a key source of data that piques our curiosity to understand, manage, innovate, and excel.
This book is supported by additional materials, including lecture slides, the complete set of figures, key references, some toy data sets used in the book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information.
Table of Contents: Social Media and Social Computing / Nodes, Ties, and Influence / Community Detection and Evaluation / Communities in Heterogeneous Networks / Social Media Mining

@book{noauthororeditoryahoo, author = {Tang‌, Lei and Liu‌, Huan}, title = {Community Detection and Mining in Social Media}, year = {2010}, url = {http://www.morganclaypool.com/doi/abs/10.2200/S00298ED1V01Y201009DMK003}, doi = {10.2200/S00298ED1V01Y201009DMK003}, keywords = {community, detection, lecture, media, social, toread}, abstract = {The past decade has witnessed the emergence of participatory Web and social media, bringing people together in many creative ways. Millions of users are playing, tagging, working, and socializing online, demonstrating new forms of collaboration, communication, and intelligence that were hardly imaginable just a short time ago. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale. This lecture, from a data mining perspective, introduces characteristics of social media, reviews representative tasks of computing with social media, and illustrates associated challenges. It introduces basic concepts, presents state-of-the-art algorithms with easy-to-understand examples, and recommends effective evaluation methods. In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media. We also demonstrate how discovered patterns of communities can be used for social media mining. The concepts, algorithms, and methods presented in this lecture can help harness the power of social media and support building socially-intelligent systems. This book is an accessible introduction to the study of community detection and mining in social media. It is an essential reading for students, researchers, and practitioners in disciplines and applications where social media is a key source of data that piques our curiosity to understand, manage, innovate, and excel.
This book is supported by additional materials, including lecture slides, the complete set of figures, key references, some toy data sets used in the book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information.
Table of Contents: Social Media and Social Computing / Nodes, Ties, and Influence / Community Detection and Evaluation / Communities in Heterogeneous Networks / Social Media Mining } }

%0 = book %A = Tang‌, Lei and Liu‌, Huan %D = 2010 %T = Community Detection and Mining in Social Media %U = http://www.morganclaypool.com/doi/abs/10.2200/S00298ED1V01Y201009DMK003

Recommender systems belong to a class of personalized information filtering technologies that aim to identify which items in a collection might be of interest to a particular user. Recommendations can be made using a variety of information sources related to both the user and the items: past user preferences, demographic information, item popularity, the metadata characteristics of the products, etc. Social bookmarking websites, with their emphasis on open collaborative information access, offer an ideal scenario for the application of recommender systems technology. They allow users to manage their favorite bookmarks online through a web interface and, in many cases, allow their users to tag the content they have added to the system with keywords. The underlying application then makes all information sharable among users. Examples of social bookmarking services include Delicious, Diigo, Furl, CiteULike, and BibSonomy. In my Ph.D. thesis I describe the work I have done on item recommendation for social bookmarking, i.e., recommending interesting bookmarks to users based on the content they bookmarked in the past. In my experiments I distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. I compare and combine the content-based aspect with the more common usage-based approaches. I evaluate my approaches on four data sets constructed from three different social bookmarking websites: BibSonomy, CiteULike, and Delicious. In addition, I investigate different combination methods for combining different algorithms and show which of those methods can successfully improve recommendation performance. Finally, I consider two growing pains that accompany the maturation of social bookmarking websites: spam and duplicate content. I examine how widespread each of these problems are for social bookmarking and how to develop effective automatic methods for detecting such unwanted content. Finally, I investigate the influence spam and duplicate content can have on item recommendation.

@phdthesis{bogers2009recommender, author = {Bogers, Toine}, title = {Recommender Systems for Social Bookmarking}, school = {Tilburg University}, address = {Tilburg, The Netherlands}, year = {2009}, url = {http://ilk.uvt.nl/~toine/phd-thesis/}, keywords = {bookmarking, dissertation, folksonomy, recommender, social, tagging, taggingsurvey}, abstract = {Recommender systems belong to a class of personalized information filtering technologies that aim to identify which items in a collection might be of interest to a particular user. Recommendations can be made using a variety of information sources related to both the user and the items: past user preferences, demographic information, item popularity, the metadata characteristics of the products, etc. Social bookmarking websites, with their emphasis on open collaborative information access, offer an ideal scenario for the application of recommender systems technology. They allow users to manage their favorite bookmarks online through a web interface and, in many cases, allow their users to tag the content they have added to the system with keywords. The underlying application then makes all information sharable among users. Examples of social bookmarking services include Delicious, Diigo, Furl, CiteULike, and BibSonomy. In my Ph.D. thesis I describe the work I have done on item recommendation for social bookmarking, i.e., recommending interesting bookmarks to users based on the content they bookmarked in the past. In my experiments I distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. I compare and combine the content-based aspect with the more common usage-based approaches. I evaluate my approaches on four data sets constructed from three different social bookmarking websites: BibSonomy, CiteULike, and Delicious. In addition, I investigate different combination methods for combining different algorithms and show which of those methods can successfully improve recommendation performance. Finally, I consider two growing pains that accompany the maturation of social bookmarking websites: spam and duplicate content. I examine how widespread each of these problems are for social bookmarking and how to develop effective automatic methods for detecting such unwanted content. Finally, I investigate the influence spam and duplicate content can have on item recommendation. } }

%0 = phdthesis %A = Bogers, Toine %C = Tilburg, The Netherlands %D = 2009 %T = Recommender Systems for Social Bookmarking %U = http://ilk.uvt.nl/~toine/phd-thesis/

@inproceedings{transybil2009, author = {Tran, D.N. and Min, B. and Li, J. and Subramanian, L.}, title = {Sybil-resilient online content rating}, year = {2009}, url = {http://scholar.google.com/scholar.bib?q=info:YVSgj4tFvzEJ:scholar.google.com/&output=citation&hl=de&as_sdt=0&scfhb=1&ct=citation&cd=0}, keywords = {factors, network, social, toread} }

%0 = inproceedings %A = Tran, D.N. and Min, B. and Li, J. and Subramanian, L. %D = 2009 %T = Sybil-resilient online content rating %U = http://scholar.google.com/scholar.bib?q=info:YVSgj4tFvzEJ:scholar.google.com/&output=citation&hl=de&as_sdt=0&scfhb=1&ct=citation&cd=0

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for social signal processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially aware computing.

@article{citeulike:3782978, author = {Vinciarelli, Alessandro and Pantic, Maja and Bourlard, Hervé}, title = {Social signal processing: Survey of an emerging domain}, journal = {Image and Vision Computing}, publisher = {Butterworth-Heinemann}, address = {Newton, MA, USA}, year = {2009}, volume = {27}, number = {12}, pages = {1743--1759}, url = {http://dx.doi.org/10.1016/j.imavis.2008.11.007}, doi = {10.1016/j.imavis.2008.11.007}, issn = {02628856}, keywords = {everyaware, introduction, signal, social, survey}, abstract = {The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for social signal processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially aware computing.} }

%0 = article %A = Vinciarelli, Alessandro and Pantic, Maja and Bourlard, Hervé %C = Newton, MA, USA %D = 2009 %I = Butterworth-Heinemann %T = Social signal processing: Survey of an emerging domain %U = http://dx.doi.org/10.1016/j.imavis.2008.11.007