With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.

@inproceedings{mueller2013recommendations, author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd}, title = {Tag Recommendations for SensorFolkSonomies}, booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings}, publisher = {ACM}, year = {2013}, pages = {New York, NY, USA}, note = {accepted for publication}, keywords = {2013, RecSys, everyaware, folksonomy, iteg, itegpub, l3s, myown, recommendation, rsweb, sensor, sitc, tag, widenoise}, abstract = {With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.} }

%0 = inproceedings %A = Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd %B = Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings %D = 2013 %I = ACM %T = Tag Recommendations for SensorFolkSonomies

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://link.springer.com/book/10.1007/978-1-4614-1894-8}, doi = {10.1007/978-1-4614-1894-8}, isbn = {978-1-4614-1893-1}, keywords = {2012, bookmarking, collaborative, folksonomy, info20, itegpub, l3s, myown, recommender, social, tagging, tagging,2012}, 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://link.springer.com/book/10.1007/978-1-4614-1894-8

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, info20, itegpub, l3s, 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

Recommendation algorithms and multi-class classifiers can support
ers of social bookmarking systems in assigning tags to their
okmarks. Content based recommenders are the usual approach for
cing the cold start problem, i.e., when a bookmark is uploaded for
e first time and no information from other users can be exploited.
this paper, we evaluate several recommendation algorithms in a
ld-start scenario on a large real-world dataset.

@inproceedings{illig2009comparison, author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems}, editor = {Wolff, Karl Erich and Palchunov, Dmitry E. and Zagoruiko, Nikolay G. and Andelfinger, Urs}, booktitle = {Knowledge Processing and Data Analysis}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2011}, volume = {6581}, pages = {136--149}, url = {http://dx.doi.org/10.1007/978-3-642-22140-8_9}, doi = {10.1007/978-3-642-22140-8_9}, isbn = {978-3-642-22139-2}, keywords = {2011, content, folksonomy, info20, itegpub, l3s, myown, recommendations, recommender, tag, tagorapub}, abstract = {Recommendation algorithms and multi-class classifiers can support
ers of social bookmarking systems in assigning tags to their
okmarks. Content based recommenders are the usual approach for
cing the cold start problem, i.e., when a bookmark is uploaded for
e first time and no information from other users can be exploited.
this paper, we evaluate several recommendation algorithms in a
ld-start scenario on a large real-world dataset.
} }

%0 = inproceedings %A = Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd %B = Knowledge Processing and Data Analysis %C = Berlin/Heidelberg %D = 2011 %I = Springer %T = A Comparison of Content-Based Tag Recommendations in Folksonomy Systems %U = http://dx.doi.org/10.1007/978-3-642-22140-8_9

Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well.

@article{benz2010query, author = {Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Stumme, Gerd}, title = {Query Logs as Folksonomies}, journal = {Datenbank-Spektrum}, year = {2010}, volume = {10}, number = {1}, pages = {15--24}, url = {http://dx.doi.org/10.1007/s13222-010-0004-8}, keywords = {2010, folksonomies, folksonomy, info20, itegpub, l3s, log, logs, logsonomy, myown}, abstract = {Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well. } }

%0 = article %A = Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Stumme, Gerd %D = 2010 %T = Query Logs as Folksonomies %U = http://dx.doi.org/10.1007/s13222-010-0004-8