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.}, acmid = {2365936}, address = {New York, NY, USA}, author = {Landia, Nikolas and Anand, Sarabjot Singh and Hotho, Andreas and J\"{a}schke, Robert and Doerfel, Stephan and Mitzlaff, Folke}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, doi = {10.1145/2365934.2365936}, interhash = {2ce2874d37fd3b90c9f6a46a7a08e94b}, intrahash = {a97bf903435d6fc4fc61e2bb7e3913b9}, isbn = {978-1-4503-1638-5}, location = {Dublin, Ireland}, numpages = {8}, pages = {1--8}, publisher = {ACM}, series = {RSWeb '12}, title = {Extending FolkRank with content data}, url = {http://doi.acm.org/10.1145/2365934.2365936}, year = 2012 } @proceedings{Mobasher:2012:2365934, 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:
(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.
(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.
(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.
(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.
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.
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.}, address = {New York, NY, USA}, author = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Hotho, Andreas}, interhash = {4a591caf39ca41da55a94a37c8c47074}, intrahash = {354947709c23c90b18dae862c46b2761}, isbn = {978-1-4503-1638-5}, location = {Dublin, Ireland}, note = 609126, publisher = {ACM}, title = {RSWeb '12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, year = 2012 } @article{pham2011development, 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.}, address = {Wien}, affiliation = {Information Systems and Database Technology, RWTH Aachen University, Aachen, Ahornstr. 55, 52056 Aachen, Germany}, author = {Pham, Manh and Klamma, Ralf and Jarke, Matthias}, doi = {10.1007/s13278-011-0024-x}, interhash = {193312234ed176aa8be9f35d4d1c4e72}, intrahash = {8ae08cacda75da80bfa5604cfce48449}, issn = {1869-5450}, journal = {Social Network Analysis and Mining}, keyword = {Computer Science}, number = 4, pages = {321--340}, publisher = {Springer}, title = {Development of computer science disciplines: a social network analysis approach}, url = {http://dx.doi.org/10.1007/s13278-011-0024-x}, volume = 1, year = 2011 } @inproceedings{conf/birthday/HothoS11, author = {Hotho, Andreas and Stumme, Gerd}, booktitle = {Foundations for the Web of Information and Services}, crossref = {conf/birthday/2011studer}, editor = {Fensel, Dieter}, ee = {http://dx.doi.org/10.1007/978-3-642-19797-0_8}, interhash = {502dc9bea95f0c581a37cd39cae2ff09}, intrahash = {845a2a6bf9a43be9e85741a6c7d2aa2d}, isbn = {978-3-642-19796-3}, pages = {143-153}, publisher = {Springer}, title = {From Semantic Web Mining to Social and Ubiquitous Mining - A Subjective View on Past, Current, and Future Research.}, url = {http://dblp.uni-trier.de/db/conf/birthday/studer2011.html#HothoS11}, year = 2011 } @article{doi:10.1080/13614568.2011.641407, author = {Cattuto, Ciro and Hotho, Andreas}, doi = {10.1080/13614568.2011.641407}, eprint = {http://www.tandfonline.com/doi/pdf/10.1080/13614568.2011.641407}, interhash = {240665a4bcaa3897e8f0f6ac150e561a}, intrahash = {609f9a53f856c8e05e193d39e87ae443}, journal = {New Review of Hypermedia and Multimedia}, number = 3, pages = {241-242}, title = {Introduction to the Special Issue on Social Linking and Hypermedia}, url = {http://www.tandfonline.com/doi/abs/10.1080/13614568.2011.641407}, volume = 17, year = 2011 } @inproceedings{Freyne:2011:WRS:2043932.2044014, 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.
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.
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
Full workshop details are available at http://www.dcs.warwick.ac.uk/~ssanand/RSWeb11/index.htm}, acmid = {2044014}, address = {New York, NY, USA}, author = {Freyne, Jill and Anand, Sarabjot Singh and Guy, Ido and Hotho, Andreas}, booktitle = {Proceedings of the fifth ACM conference on Recommender systems}, doi = {10.1145/2043932.2044014}, interhash = {6171f01a8f8cd063ec257cb4809801a6}, intrahash = {11fe6dd3da33f0aa0a40c998e5193ab8}, isbn = {978-1-4503-0683-6}, location = {Chicago, Illinois, USA}, numpages = {2}, pages = {383--384}, publisher = {ACM}, series = {RecSys '11}, title = {3rd workshop on recommender systems and the social web}, url = {http://doi.acm.org/10.1145/2043932.2044014}, year = 2011 } @article{Haustein2011446, 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.}, author = {Haustein, Stefanie and Siebenlist, Tobias}, doi = {10.1016/j.joi.2011.04.002}, interhash = {13fe59aae3d6ef95b529ffe00ede4126}, intrahash = {60170943fb293bcb54754710ec9dced1}, issn = {1751-1577}, journal = {Journal of Informetrics}, number = 3, pages = {446 - 457}, title = {Applying social bookmarking data to evaluate journal usage}, url = {http://www.sciencedirect.com/science/article/pii/S1751157711000393}, volume = 5, year = 2011 } @book{balbymarinho2012recommender, 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.}, 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.}, interhash = {0bb7f0588cd690d67cc73e219a3a24fa}, intrahash = {87d6883ebd98e8810be45d7e7e4ade96}, isbn = {978-1-4614-1893-1}, month = feb, publisher = {Springer}, series = {SpringerBriefs in Electrical and Computer Engineering}, title = {Recommender Systems for Social Tagging Systems}, url = {http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1}, year = 2012 } @incollection{jaeschke2012challenges, 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.}, address = {Berlin/Heidelberg}, affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany}, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {Recommender Systems for the Social Web}, doi = {10.1007/978-3-642-25694-3_3}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, interhash = {75b1a6f54ef54d0126d0616b5bf77563}, intrahash = {7d41d332cccc3e7ba8e7dadfb7996337}, isbn = {978-3-642-25694-3}, pages = {65--87}, publisher = {Springer}, series = {Intelligent Systems Reference Library}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}, volume = 32, year = 2012 } @proceedings{conf/ht/2010msmmuse, booktitle = {MSM/MUSE}, editor = {Atzmueller, Martin and Hotho, Andreas and Strohmaier, Markus and Chin, Alvin}, ee = {http://dx.doi.org/10.1007/978-3-642-23599-3}, interhash = {2be9c4f31fd94e24d902520195b653d3}, intrahash = {4cf42ebabd9a670c70bee456affda285}, isbn = {978-3-642-23598-6}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, 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}, url = {http://dblp.uni-trier.de/db/conf/ht/msmmuse2010.html}, volume = 6904, year = 2011 } @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}, booktitle = {it - Information Technology}, comment = {doi: 10.1524/itit.2011.0631}, doi = {10.1524/itit.2011.0631}, interhash = {e57bff1f73b74e6f1fe79e4b40956c35}, intrahash = {1dc34c1620c45a9bbd548bb73f989aea}, issn = {16112776}, journal = {it - Information Technology}, month = may, number = 3, pages = {101--107}, publisher = {Oldenbourg Wissenschaftsverlag GmbH}, title = {Enhancing Social Interactions at Conferences}, url = {http://dx.doi.org/10.1524/itit.2011.0631}, volume = 53, year = 2011 } @inproceedings{benz2011measuring, author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus}, booktitle = {Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation}, interhash = {33a2078f3836293d71c449d5376fc440}, intrahash = {923d369285422c758398cbe92e3532cd}, title = {One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata}, year = 2011 } @inproceedings{atzmueller2011towards, author = {Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 4th international workshop on Social Data on the Web (SDoW2011)}, editor = {Passant, Alexandre and Fernández, Sergio and Breslin, John and Bojārs, Uldis}, interhash = {65222f0ccc23063a2a15c0a7fd5513a0}, intrahash = {a47a41658592202811f0139d4bb65871}, title = {Towards Mining Semantic Maturity in Social Bookmarking Systems}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/atzmueller2011towards.pdf}, year = 2011 } @article{burke2011recommendation, 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.}, author = {Burke, Robin and Gemmell, Jonathan and Hotho, Andreas and Jäschke, Robert}, interhash = {3089ca25de28ef0bc80bcdebd375a6f9}, intrahash = {41dbb2c9f71440c9aa402f8966117979}, journal = {AI Magazine}, number = 3, pages = {46--56}, publisher = {Association for the Advancement of Artificial Intelligence}, title = {Recommendation in the Social Web}, url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2373}, volume = 32, year = 2011 } @inproceedings{bullock2011tagging, author = {Bullock, Beate Navarro and Jäschke, Robert and Hotho, Andreas}, booktitle = {Proceedings of the ACM WebSci'11}, interhash = {7afaa67dfeb07f7e0b85abf2be61aff1}, intrahash = {493e03868a98f498628cad31f9320e9f}, month = {June}, title = {Tagging data as implicit feedback for learning-to-rank}, url = {http://journal.webscience.org/463/}, year = 2011 } @phdthesis{jschke2011formal, address = {[Amsterdam]}, author = {Jäschke, Robert}, interhash = {dcb2cd1cd72ae45d77c4d8755d199405}, intrahash = {1ac91a922a872523de0ce8d4984e53a3}, isbn = {9781607507079 1607507072 9783898383325 3898383326}, pages = {--}, publisher = {IOS Press}, refid = {707172013}, title = {Formal concept analysis and tag recommendations in collaborative tagging systems}, url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=9783898383325}, year = 2011 } @phdthesis{bogers2009recommender, 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. }, address = {Tilburg, The Netherlands}, author = {Bogers, Toine}, interhash = {65b74dcabaa583a48469f3dec2ec1f62}, intrahash = {b02daac1201473600b7c8d2553865b4a}, month = dec, school = {Tilburg University}, title = {Recommender Systems for Social Bookmarking}, url = {http://ilk.uvt.nl/~toine/phd-thesis/}, year = 2009 } @inproceedings{transybil2009, author = {Tran, D.N. and Min, B. and Li, J. and Subramanian, L.}, interhash = {34d39d14be357a65eefa8207a3fb5856}, intrahash = {40c3dea03e3e4c561db6bc4b34c6f3da}, organization = {Citeseer}, title = {Sybil-resilient online content rating}, 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}, year = 2009 } @article{citeulike:3782978, 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.}, address = {Newton, MA, USA}, author = {Vinciarelli, Alessandro and Pantic, Maja and Bourlard, Herv\'{e}}, citeulike-article-id = {3782978}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1621144.1621310}, citeulike-linkout-1 = {http://dx.doi.org/10.1016/j.imavis.2008.11.007}, citeulike-linkout-2 = {http://linkinghub.elsevier.com/retrieve/pii/S0262885608002485}, day = 03, doi = {10.1016/j.imavis.2008.11.007}, interhash = {639f01cfa95f7da21fd65afae66027a5}, intrahash = {0a4122235c6b50c760ff27aafc990999}, issn = {02628856}, journal = {Image and Vision Computing}, month = nov, number = 12, pages = {1743--1759}, posted-at = {2008-12-12 17:48:38}, priority = {2}, publisher = {Butterworth-Heinemann}, title = {Social signal processing: Survey of an emerging domain}, url = {http://dx.doi.org/10.1016/j.imavis.2008.11.007}, volume = 27, year = 2009 }