@article{Lü20121, abstract = {The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has great scientific depth and combines diverse research fields which makes it interesting for physicists as well as interdisciplinary researchers.}, author = {Lü, Linyuan and Medo, Matúš and Yeung, Chi Ho and Zhang, Yi-Cheng and Zhang, Zi-Ke and Zhou, Tao}, doi = {10.1016/j.physrep.2012.02.006}, interhash = {408fbf13302368693d501271268cda03}, intrahash = {9594d6b87d49d22b783b9c95da1f59af}, issn = {0370-1573}, journal = {Physics Reports}, note = {Recommender Systems}, number = 1, pages = {1 - 49}, title = {Recommender systems}, url = {http://www.sciencedirect.com/science/article/pii/S0370157312000828}, volume = 519, year = 2012 } @misc{weston2012latent, abstract = {Retrieval tasks typically require a ranking of items given a query. Collaborative filtering tasks, on the other hand, learn to model user's preferences over items. In this paper we study the joint problem of recommending items to a user with respect to a given query, which is a surprisingly common task. This setup differs from the standard collaborative filtering one in that we are given a query x user x item tensor for training instead of the more traditional user x item matrix. Compared to document retrieval we do have a query, but we may or may not have content features (we will consider both cases) and we can also take account of the user's profile. We introduce a factorized model for this new task that optimizes the top-ranked items returned for the given query and user. We report empirical results where it outperforms several baselines.}, author = {Weston, Jason and Wang, Chong and Weiss, Ron and Berenzweig, Adam}, interhash = {d0ea194dd0e3a6f35c578439efcb8bff}, intrahash = {79c6771a9b032497635d5f39a39e921a}, note = {cite arxiv:1206.4603Comment: ICML2012}, title = {Latent Collaborative Retrieval}, url = {http://arxiv.org/abs/1206.4603}, year = 2012 } @inproceedings{conf/www/SenVR09, author = {Sen, Shilad and Vig, Jesse and Riedl, John}, booktitle = {WWW}, crossref = {conf/www/2009}, editor = {Quemada, Juan and León, Gonzalo and Maarek, Yoëlle S. and Nejdl, Wolfgang}, ee = {http://doi.acm.org/10.1145/1526709.1526800}, interhash = {4968b29a544394a5f9acd1bb8916e230}, intrahash = {8d38bdb12f6f2f89bd3c34d200e48b72}, isbn = {978-1-60558-487-4}, pages = {671-680}, publisher = {ACM}, title = {Tagommenders: connecting users to items through tags.}, url = {http://dblp.uni-trier.de/db/conf/www/www2009.html#SenVR09}, year = 2009 } @proceedings{Gunawardana2935, author = {Gunawardana, Asela and Shani, Guy}, interhash = {441df9b673faf85aecc45babd8883069}, intrahash = {49600df05a884106989d71dedcaa7e1b}, page = {2935−2962}, series = 2935, title = { A Survey of Accuracy Evaluation Metrics of Recommendation Tasks }, url = {http://jmlr.csail.mit.edu/papers/v10/gunawardana09a.html}, volume = {v10}, year = 2009 } @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 } @incollection{citeulike:6386729, abstract = {Collaborative tagging can help users organize, share and retrieve information in an easy and quick way. For the collaborative tagging information implies user's important personal preference information, it can be used to recommend personalized items to users. This paper proposes a novel tag-based collaborative filtering approach for recommending personalized items to users of online communities that are equipped with tagging facilities. Based on the distinctive three dimensional relationships among users, tags and items, a new similarity measure method is proposed to generate the neighborhood of users with similar tagging behavior instead of similar implicit ratings. The promising experiment result shows that by using the tagging information the proposed approach outperforms the standard user and item based collaborative filtering approaches.}, address = {Berlin, Heidelberg}, author = {Liang, Huizhi and Xu, Yue and Li, Yuefeng and Nayak, Richi}, booktitle = {Rough Sets and Knowledge Technology }, chapter = 84, citeulike-article-id = {6386729}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-02962-2\_84}, citeulike-linkout-1 = {http://www.springerlink.com/content/f66k11352q386379}, doi = {10.1007/978-3-642-02962-2\_84}, editor = {Wen, Peng and Li, Yuefeng and Polkowski, Lech and Yao, Yiyu and Tsumoto, Shusaku and Wang, Guoyin}, interhash = {80e8a1d0263296925609dbd5b72b7d48}, intrahash = {bf98d7c1fee5f2f188f529701e70199f}, isbn = {978-3-642-02961-5}, pages = {666--673}, posted-at = {2009-12-15 15:06:20}, priority = {2}, publisher = {Springer Berlin Heidelberg}, title = {Tag Based Collaborative Filtering for Recommender Systems}, url = {http://dx.doi.org/10.1007/978-3-642-02962-2\_84}, volume = 5589, year = 2009 } @inproceedings{1502661, abstract = {While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.}, address = {New York, NY, USA}, author = {Vig, Jesse and Sen, Shilad and Riedl, John}, booktitle = {IUI '09: Proceedingsc of the 13th international conference on Intelligent user interfaces}, doi = {http://doi.acm.org/10.1145/1502650.1502661}, interhash = {a6d866cf13c75130c1969c9e40606fd1}, intrahash = {1e74fa227a24f49d8f6b17a02ea96db5}, isbn = {978-1-60558-168-2}, location = {Sanibel Island, Florida, USA}, pages = {47--56}, publisher = {ACM}, title = {Tagsplanations: explaining recommendations using tags}, url = {http://portal.acm.org/citation.cfm?id=1502650.1502661}, year = 2008 } @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 } @inproceedings{1390423, address = {New York, NY, USA}, author = {Song, Yang and Zhuang, Ziming and Li, Huajing and Zhao, Qiankun and Li, Jia and Lee, Wang-Chien and Giles, C. Lee}, booktitle = {SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval}, doi = {http://doi.acm.org/10.1145/1390334.1390423}, interhash = {e6505664e875de06d98a6e787d4367d1}, intrahash = {525a37f6ef3d81a81686b515a148b88b}, isbn = {978-1-60558-164-4}, location = {Singapore, Singapore}, pages = {515--522}, publisher = {ACM}, title = {Real-time automatic tag recommendation}, url = {http://portal.acm.org/citation.cfm?id=1390334.1390423}, 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{1454053, address = {New York, NY, USA}, author = {Bogers, Toine and van den Bosch, Antal}, booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems}, doi = {http://doi.acm.org/10.1145/1454008.1454053}, interhash = {692eb1215676da39997ad861b681c450}, intrahash = {9d0d8ca850db6cf6177efc66e16785b7}, isbn = {978-1-60558-093-7}, location = {Lausanne, Switzerland}, pages = {287--290}, publisher = {ACM}, title = {Recommending scientific articles using citeulike}, url = {http://portal.acm.org/citation.cfm?id=1454053}, year = 2008 } @inproceedings{1454017, address = {New York, NY, USA}, author = {Symeonidis, Panagiotis and Nanopoulos, Alexandros and Manolopoulos, Yannis}, booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems}, doi = {http://doi.acm.org/10.1145/1454008.1454017}, interhash = {8ee38f4ffc05845fcb98f121fb265d48}, intrahash = {e93afe409833a632af02290bbe134cba}, isbn = {978-1-60558-093-7}, location = {Lausanne, Switzerland}, pages = {43--50}, publisher = {ACM}, title = {Tag recommendations based on tensor dimensionality reduction}, url = {http://portal.acm.org/citation.cfm?id=1454017}, 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{295795, address = {Menlo Park, CA, USA}, author = {Basu, Chumki and Hirsh, Haym and Cohen, William}, booktitle = {AAAI '98/IAAI '98: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence}, interhash = {8baccca5d4a5001d79070bbdd5439a84}, intrahash = {90f4b7eab8a7a308c6e077a993cd19d8}, isbn = {0-262-51098-7}, location = {Madison, Wisconsin, United States}, pages = {714--720}, publisher = {American Association for Artificial Intelligence}, title = {Recommendation as classification: using social and content-based information in recommendation}, url = {ftp://ftp.cs.rutgers.edu/pub/hirsh/papers/1998/aaai1.ps}, year = 1998 } @article{rashid2005irb, author = {Rashid, A. M. and Karypis, G. and Riedl, J.}, interhash = {9fcc40d7537a7d10df92a3e2effb8f69}, intrahash = {65e6a92489e5190ca5129532d2d138fd}, journal = {Proceedings of the SIAM International Conference on Data Mining}, title = {{Influence in ratings-based recommender systems: An algorithm-independent approach}}, url = {http://www.grouplens.org/papers/pdf/RashidAl_siam05.pdf}, year = 2005 } @inproceedings{breese98empirical, author = {Breese, John S. and Heckerman, David and Kadie, Carl}, booktitle = {Proceedings of the 14$^{th}$ Conference on Uncertainty in Artificial Intelligence}, interhash = {593f72dfa20e4b7b5b16205479989020}, intrahash = {82cd7b6c312f4181b1d05adb10c1d56a}, pages = {43-52}, title = {Empirical Analysis of Predictive Algorithms for Collaborative Filtering}, year = 1998 } @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 } @article{ieKey, abstract = {Empfehlungssysteme tragen Inhalte individuell an Nutzer im WWW heran, basierend auf deren konkreten Bedürfnissen, Vorlieben und Interessen. Solche Systeme können Produkte, Services, Nutzer (mit analogen Interessen) uvm. vorschlagen und stellen damit – gerade im Web 2.0-Zeitalter – eine besondere Form der Personalisierung sowie des social networking dar. Damit bieten Empfehlungssysteme Anbietern im ECommerce einen entscheidenden Marktvorteil, weshalb die Auswertung der Kundendaten bei großen Firmen wie Amazon, Google oder Ebay eine hohe Priorität besitzt. Aus diesem Grund wird im vorliegenden Artikel auf die Ansätze von Empfehlungssystemen, welche auf unterschiedliche Weise die Bedürfnisse des Nutzers aufgreifen bzw. „vorausahnen“ und ihm Vorschläge (aus verschiedenen Bereichen) unterbreiten können, eingegangen. Der Artikel liefert eine Definition und Darstellung der Arbeitsweisen von Empfehlungssystemen. Dabei werden die verschiedenen Methodiken jener Dienste vergleichend erläutert, um ihre jeweiligen Vor- und Nachteile deutlich zu machen. Außerdem wird der Ontologie- und Folksonomy-Einsatz innerhalb von Empfehlungssystemen beleuchtet, um Chancen und Risiken der Anwendung von Methoden der Wissensrepräsentation für zukünftige Forschungsarbeiten einschätzen zu können. Recommender Systems in an Information Science View – The State of the Art Recommender systems offer content individually to users in the WWW, based on their concrete needs, preferences and interests. Those systems can propose products, services, users (with analogous interests), etc.) and represent a special form of personalisation as well as of social networking – exactly in the Web 2.0 age. Recommender systems offer e.g. suppliers in the e-commerce a crucial market advantage. So, the evaluation of the customer data has high priority at big companies like Amazon, Google or Ebay. For this reason we engaged in recommender systems, which take up the user’s needs in different ways, to “anticipate“ needs and make suggestions (from different areas) to the user. This review article achieves a definition and representation of operations and methods of recommender systems. Exactly the different methodologies of those services should be expounded comparativly on that occasion in order to represent advantages and disadvantages. The use of ontologies and folksonomies as implementations in recommender systems is portrayed in order to be able to take into consideration chances and risks of the application of knowledge representation methods for future researches.}, author = {Höhfeld, Stefanie and Kwiatkowski, Melanie}, interhash = {e6216d6334aad0ebcb7190eb661c83f6}, intrahash = {501849ade58a25831e72519f6add1313}, journal = {IWP-Information Wissenschaft & Praxis}, number = 5, pages = {265-276}, title = {Empfehlungssysteme aus informationswissenschaftlicher Sicht-State of the Art}, url = {http://wwwalt.phil-fak.uni-duesseldorf.de/infowiss/admin/public_dateien/files/58/1189509550empfehlung.pdf}, volume = 58, year = 2007 } @techreport{Vojnovic, author = {Vojnovic, M. and Cruise, J. and Gunawardena, D. and Marbach, P.}, date = {Feb 2007}, howpublished = {MSR-TR-2007-06}, interhash = {687129a4106fab9a2cd1c032ae52fc73}, intrahash = {1af9877eebe38cdedc6578967ae76d8a}, tech = {TR-2007-06}, title = {Ranking and Suggesting Tags in Collaborative Tagging Applications}, url = {http://research.microsoft.com/~milanv/tagbooster.aspx}, year = 2007 }