@article{citeulike:8506476,
  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.}},
  address = {Los Alamitos, CA, USA},
  author = {Santander, Denis P. and Brusilovsky, Peter},
  citeulike-article-id = {8506476},
  citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/WI-IAT.2010.261},
  citeulike-linkout-1 = {http://dx.doi.org/10.1109/WI-IAT.2010.261},
  doi = {10.1109/WI-IAT.2010.261},
  interhash = {dd320da969151c01cf270976c0803274},
  intrahash = {2c8764f2fe11ef1ae43fc0a5b51301ae},
  isbn = {978-0-7695-4191-4},
  journal = {Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on},
  pages = {136--142},
  posted-at = {2011-01-05 00:19:36},
  priority = {0},
  publisher = {IEEE Computer Society},
  title = {{Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles}},
  url = {http://dx.doi.org/10.1109/WI-IAT.2010.261},
  volume = 1,
  year = 2010
}

@inproceedings{heymann2008social,
  abstract = {In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.},
  address = {New York, NY, USA},
  author = {Heymann, Paul and Ramage, Daniel and Garcia-Molina, Hector},
  booktitle = {SIGIR '08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
  doi = {http://doi.acm.org/10.1145/1390334.1390425},
  interhash = {bb9455c80cc9bd8cf95c951a1318dabc},
  intrahash = {0e6023e192f539fe4fce9894b1fbca5a},
  isbn = {978-1-60558-164-4},
  location = {Singapore, Singapore},
  pages = {531--538},
  publisher = {ACM},
  title = {Social tag prediction},
  url = {http://portal.acm.org/citation.cfm?id=1390334.1390425},
  year = 2008
}

@incollection{citeulike:3149792,
  abstract = {The motivation behind many Information Retrieval systems is to identify and present relevant information to people given their current goals and needs. Learning about user preferences and access patterns recent technologies make it possible to model user information needs and adapt services to meet these needs. In previous work we have presented ASSIST, a general-purpose platform which incorporates various types of social support into existing information access systems and reported on our deployment experience in a highly goal driven environment (ACM Digital Library). In this work we present our experiences in applying ASSIST to a domain where goals are less focused and where casual exploration is more dominant; YouTube. We present a general study of YouTube access patterns and detail how the ASSIST architecture affected the access patterns of users in this domain.},
  author = {Coyle, Maurice and Freyne, Jill and Brusilovsky, Peter and Smyth, Barry},
  citeulike-article-id = {3149792},
  doi = {http://dx.doi.org/10.1007/978-3-540-70987-9\_12},
  interhash = {487512d7286ca43ca9b96ee4a0efc198},
  intrahash = {f75eb556b19abd7b399f2f27ae49cb1c},
  journal = {Adaptive Hypermedia and Adaptive Web-Based Systems},
  pages = {93--102},
  posted-at = {2008-10-13 00:16:23},
  priority = {2},
  title = {Social Information Access for the Rest of Us: An Exploration of Social YouTube},
  url = {http://www.springerlink.com/content/6h410u3w4836v866/},
  year = 2008
}

@inproceedings{1458098,
  address = {New York, NY, USA},
  author = {Song, Yang and Zhang, Lu and Giles, C. Lee},
  booktitle = {CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining},
  doi = {http://doi.acm.org/10.1145/1458082.1458098},
  interhash = {5c03bc1e658b6d44f053944418bdaec3},
  intrahash = {d330a3537b4a14fbd40661424ec8e465},
  isbn = {978-1-59593-991-3},
  location = {Napa Valley, California, USA},
  pages = {93--102},
  publisher = {ACM},
  title = {A sparse gaussian processes classification framework for fast tag suggestions},
  url = {http://portal.acm.org/citation.cfm?id=1458098},
  year = 2008
}

@inproceedings{Byde2007,
  abstract = {This short paper describes a novel technique for generating personalized tag recommendations for users of social book- marking sites such as del.icio.us. Existing techniques recom- mend tags on the basis of their popularity among the group of all users; on the basis of recent use; or on the basis of simple heuristics to extract keywords from the url being tagged. Our method is designed to complement these approaches, and is based on recommending tags from urls that are similar to the one in question, according to two distinct similarity metrics, whose principal utility covers complementary cases.},
  author = {Byde, Andrew and Wan, Hui and Cayzer, Steve},
  booktitle = {Proceedings of the International Conference on Weblogs and Social Media},
  interhash = {38aaca7e5b9c508a5901f4109dabaa69},
  intrahash = {157846898c1c2a65c265a913ebac115a},
  month = {March},
  priority = {5},
  title = {Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics},
  url = {http://www.icwsm.org/papers/paper47.html},
  year = 2007
}

@article{keyhere,
  abstract = {Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different users’ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods. ER  -},
  author = {Xu, Yanfei and Zhang, Liang and Liu, Wei},
  interhash = {edf999afa5a0ff81e53b0c859b466659},
  intrahash = {5fbd24f07fe8784b516e69b0eb3192f3},
  journal = {Frontiers of WWW Research and Development - APWeb 2006},
  pages = {733--738},
  title = {Cubic Analysis of Social Bookmarking for Personalized Recommendation},
  url = {http://dx.doi.org/10.1007/11610113_66},
  year = 2006
}