@inproceedings{doerfel2012leveraging, 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.}, acmid = {2365937}, address = {New York, NY, USA}, author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, doi = {10.1145/2365934.2365937}, interhash = {beb2c81daf975eeed6e01e1b412196b1}, intrahash = {d3e6fa8023b173228a959914affc8d73}, isbn = {978-1-4503-1638-5}, location = {Dublin, Ireland}, numpages = {8}, pages = {9--16}, publisher = {ACM}, series = {RSWeb '12}, title = {Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation}, url = {http://doi.acm.org/10.1145/2365934.2365937}, year = 2012 } @inproceedings{wetzker2009hybrid, abstract = {In this paper we consider the problem of item recommendation in collaborative tagging communities, so called folksonomies, where users annotate interesting items with tags. Rather than following a collaborative filtering or annotation-based approach to recommendation, we extend the probabilistic latent semantic analysis (PLSA) approach and present a unified recommendation model which evolves from item user and item tag co-occurrences in parallel. The inclusion of tags reduces known collaborative filtering problems related to overfitting and allows for higher quality recommendations. Experimental results on a large snapshot of the delicious bookmarking service show the scalability of our approach and an improved recommendation quality compared to two-mode collaborative or annotation based methods.}, acmid = {1506255}, address = {New York, NY, USA}, author = {Wetzker, Robert and Umbrath, Winfried and Said, Alan}, booktitle = {Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval}, doi = {10.1145/1506250.1506255}, interhash = {5a4e686feaa38748f7eac2c8a3afe51e}, intrahash = {733e1968baace40173bd30486b49a8f0}, isbn = {978-1-60558-430-0}, location = {Barcelona, Spain}, numpages = {5}, pages = {25--29}, publisher = {ACM}, series = {ESAIR '09}, title = {A hybrid approach to item recommendation in folksonomies}, url = {http://doi.acm.org/10.1145/1506250.1506255}, year = 2009 } @article{gemmell2012resource, abstract = {Social annotation systems enable the organization of online resources with user-defined keywords. Collectively these annotations provide a rich information space in which users can discover resources, organize and share their finds, and connect to other users with similar interests. However, the size and complexity of these systems can lead to information overload and reduced utility for users. For these reasons, researchers have sought to apply the techniques of recommender systems to deliver personalized views of social annotation systems. To date, most efforts have concentrated on the problem of tag recommendation – personalized suggestions for possible annotations. Resource recommendation has not received the same systematic evaluation, in part because the task is inherently more complex. In this article, we provide a general formulation for the problem of resource recommendation in social annotation systems that captures these variants, and we evaluate two cases: basic resource recommendation and tag-specific resource recommendation. We also propose a linear-weighted hybrid framework for resource recommendation. Using six real-world datasets, we show that its integrative approach is essential for this recommendation task and provides the most adaptability given the varying data characteristics in different social annotation systems. We find that our algorithm is more effective than other more mathematically-complex techniques and has the additional advantages of flexibility and extensibility.}, author = {Gemmell, Jonathan and Schimoler, Thomas and Mobasher, Bamshad and Burke, Robin}, doi = {10.1016/j.jcss.2011.10.006}, interhash = {e7a4b630500c6a468c40d0e63ee31455}, intrahash = {de0e3910bd4932b63e5ba6058e5cee45}, issn = {0022-0000}, journal = {Journal of Computer and System Sciences}, number = 4, pages = {1160 - 1174}, title = {Resource recommendation in social annotation systems: A linear-weighted hybrid approach}, url = {http://www.sciencedirect.com/science/article/pii/S0022000011001127}, volume = 78, year = 2012 } @inproceedings{shepitsen2008personalized, abstract = {Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.}, acmid = {1454048}, address = {New York, NY, USA}, author = {Shepitsen, Andriy and Gemmell, Jonathan and Mobasher, Bamshad and Burke, Robin}, booktitle = {Proceedings of the 2008 ACM conference on Recommender systems}, doi = {10.1145/1454008.1454048}, interhash = {c9028129dd7cd8314673bd64cbb6198e}, intrahash = {0700627147554148d7e6db5979aa27d2}, isbn = {978-1-60558-093-7}, location = {Lausanne, Switzerland}, numpages = {8}, pages = {259--266}, publisher = {ACM}, series = {RecSys '08}, title = {Personalized recommendation in social tagging systems using hierarchical clustering}, url = {http://doi.acm.org/10.1145/1454008.1454048}, year = 2008 } @inproceedings{McNee:2006:DLS:1180875.1180903, abstract = {If recommenders are to help people be more productive, they need to support a wide variety of real-world information seeking tasks, such as those found when seeking research papers in a digital library. There are many potential pitfalls, including not knowing what tasks to support, generating recommendations for the wrong task, or even failing to generate any meaningful recommendations whatsoever. We posit that different recommender algorithms are better suited to certain information seeking tasks. In this work, we perform a detailed user study with over 130 users to understand these differences between recommender algorithms through an online survey of paper recommendations from the ACM Digital Library. We found that pitfalls are hard to avoid. Two of our algorithms generated 'atypical' recommendations recommendations that were unrelated to their input baskets. Users reacted accordingly, providing strong negative results for these algorithms. Results from our 'typical' algorithms show some qualitative differences, but since users were exposed to two algorithms, the results may be biased. We present a wide variety of results, teasing out differences between algorithms. Finally, we succinctly summarize our most striking results as "Don't Look Stupid" in front of users.}, acmid = {1180903}, address = {New York, NY, USA}, author = {McNee, Sean M. and Kapoor, Nishikant and Konstan, Joseph A.}, booktitle = {Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work}, doi = {10.1145/1180875.1180903}, interhash = {24be686d042a3a4a710d9ff22dee0f2e}, intrahash = {7775150ca225770019bd94db9be5db40}, isbn = {1-59593-249-6}, location = {Banff, Alberta, Canada}, numpages = {10}, pages = {171--180}, publisher = {ACM}, series = {CSCW '06}, title = {Don't look stupid: avoiding pitfalls when recommending research papers}, url = {http://doi.acm.org/10.1145/1180875.1180903}, year = 2006 } @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 }