@article{zhang2006, title = {{Mining search engine query logs for query recommendation}}, author = {Z. Zhang and O. Nasraoui}, journal = {Proceedings of the 15th international conference on World Wide Web}, pages = {1039--1040}, publisher = {ACM Press New York, NY, USA}, year = 2006, biburl = {http://www.bibsonomy.org/bibtex/2f4873abd71cd109213b349c554cb376d/wnpxrz}, keywords = {query recommendation log ir search recommendersystems} } @inproceedings{conf/sww/MiddletonASR02, title = {Exploiting Synergy Between Ontologies and Recommender Systems.}, author = {Stuart E. Middleton and Harith Alani and Nigel Shadbolt and David De Roure}, booktitle = {Semantic Web Workshop}, crossref = {conf/sww/2002}, editor = {Martin Frank and Natasha F. Noy and Steffen Staab}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, volume = 55, year = 2002, url = {http://dblp.uni-trier.de/db/conf/sww/sww2002.html#MiddletonASR02}, ee = {http://SunSITE.Informatik.RWTH-Aachen.DE/Publications/CEUR-WS/Vol-55/middleton.pdf}, date = {2003-04-02}, description = {dblp}, biburl = {http://www.bibsonomy.org/bibtex/2816daaef7845122e39b0fbaba9a4ee79/wnpxrz}, keywords = {ontology recommendersystems} } @article{herlocker2004evaluating, title = {Evaluating collaborative filtering recommender systems}, address = {New York, NY, USA}, author = {Jonathan L. Herlocker and Joseph A. Konstan and Loren G. Terveen and John T. Riedl}, journal = {ACM Trans. Inf. Syst.}, number = 1, pages = {5--53}, publisher = {ACM Press}, volume = 22, year = 2004, url = {http://portal.acm.org/citation.cfm?id=963770.963772}, issn = {1046-8188}, doi = {http://doi.acm.org/10.1145/963770.963772}, description = {Evaluating collaborative filtering recommender systems}, abstract = {Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.}, biburl = {http://www.bibsonomy.org/bibtex/2bdd3980bb3c297d1b84ceb0c7729d397/wnpxrz}, keywords = {collaborative evaluation recommendersystems filtering} } @article{keyhere, title = {Exploit Semantic Information for Category Annotation Recommendation in Wikipedia}, author = {Yang Wang and Haofen Wang and Haiping Zhu and Yong Yu}, journal = {Natural Language Processing and Information Systems}, pages = {48--60}, year = 2007, url = {http://dx.doi.org/10.1007/978-3-540-73351-5_5}, description = {SpringerLink - Book Chapter}, abstract = {Compared with plain-text resources, the ones in “semi-semantic” web sites, such as Wikipedia, contain high-level semantic information which will benefit various automatically annotating tasks on themself. In this paper, we propose a “collaborativeannotating” approach to automatically recommend categories for a Wikipedia article by reusing category annotations from itsmost similar articles and ranking these annotations by their confidence. In this approach, four typical semantic featuresin Wikipedia, namely incoming link, outgoing link, section heading and template item, are investigated and exploited as therepresentation of articles to feed the similarity calculation. The experiment results have not only proven that these semanticfeatures improve the performance of category annotating, with comparison to the plain text feature; but also demonstratedthe strength of our approach in discovering missing annotations and proper level ones for Wikipedia articles.}, biburl = {http://www.bibsonomy.org/bibtex/2670657d4fa40539e790a03d602b1894a/wnpxrz}, keywords = {wikipedia recommendersystems imported} } @misc{terveen01beyond, title = {Beyond Recommender Systems: Helping People Help Each Other}, author = {L. Terveen and W. Hill}, year = 2001, url = {citeseer.ist.psu.edu/terveen01beyond.html}, description = {Beyond Recommender Systems: Helping People Help Each Other - Terveen, Hill (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/2e26b0d4305079fae673c8dcdb1999d68/wnpxrz}, keywords = {collaborative filtering imported recommendersystems} } @article{Linetal02, title = {Efficient adaptive-support association rule mining for recommender systems}, author = {W. Lin and S.A. Alvarez and C. Ruiz}, journal = {Data Mining and Knowledge Discovery}, pages = {83--105}, volume = 6, year = 2002, biburl = {http://www.bibsonomy.org/bibtex/21bd636292bc53fb0d8cdd71cb5a10adc/wnpxrz}, keywords = {recommendersystems mining rule} } @inproceedings{adrian+2007a, title = {ConTag: A semantic tag recommendation system}, author = {Benjamin Adrian and Leo Sauermann and Thomas Roth-Berghofer}, booktitle = {Proceedings of I-Semantics' 07}, editor = {Tassilo Pellegrini and Sebastian Schaffert}, pages = {pp. 297-304}, publisher = {JUCS}, year = 2007, url = {http://www.dfki.uni-kl.de/~sauermann/papers/horak+2007a.pdf}, timestamp = {2007.09.12}, pdf = {adrian+2007a.pdf}, owner = {sauermann}, doi = {ISSN 0948-6968}, description = {iknow2007 publications}, abstract = {ConTag is an approach to generate semantic tag recommendations for documents based on Semantic Web ontologies and Web 2.0 services. We designed and implemented a process to normalize documents to RDF format, extract document topics using Web 2.0 services and finally match extracted topics to a Semantic Web ontology. Due to ConTag we are able to show that the information provided by Web 2.0 services in combination with a Semantic Web ontology enables the generation of relevant semantic tag recommendations for documents. The main contribution of this work is a semantic tag recommendation process based on a choreography of Web 2.0 services.}, biburl = {http://www.bibsonomy.org/bibtex/2baf236eafcb9b39d34339a798bfef58b/wnpxrz}, keywords = {tagging recommendersystems tags} } @article{rashid2005irb, title = {{Influence in ratings-based recommender systems: An algorithm-independent approach}}, author = {A. M. Rashid and G. Karypis and J. Riedl}, journal = {Proceedings of the SIAM International Conference on Data Mining}, year = 2005, url = {http://www.grouplens.org/papers/pdf/RashidAl_siam05.pdf}, biburl = {http://www.bibsonomy.org/bibtex/265e6a92489e5190ca5129532d2d138fd/wnpxrz}, keywords = {recommendersystems} } @inproceedings{295795, title = {Recommendation as classification: using social and content-based information in recommendation}, address = {Menlo Park, CA, USA}, author = {Chumki Basu and Haym Hirsh and William Cohen}, booktitle = {AAAI '98/IAAI '98: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence}, pages = {714--720}, publisher = {American Association for Artificial Intelligence}, year = 1998, url = {ftp://ftp.cs.rutgers.edu/pub/hirsh/papers/1998/aaai1.ps}, location = {Madison, Wisconsin, United States}, isbn = {0-262-51098-7}, biburl = {http://www.bibsonomy.org/bibtex/290f4b7eab8a7a308c6e077a993cd19d8/wnpxrz}, keywords = {classification filtering recommendersystems content collaborative social} } @inproceedings{Niwa:2006, title = {Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions}, author = {S. Niwa and Takuo Doi and S. Honiden}, booktitle = {Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on}, pages = {388- 393}, year = 2006, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1611624}, isbn = {0-7695-2497-4}, doi = {10.1109/ITNG.2006.140}, description = {Welcome to IEEE Xplore 2.0: Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions}, abstract = {There have been many attempts to construct web page recommender systems using collaborative filtering. But the domains these systems can cover are very restricted because it is very difficult to assemble user preference data to web pages, and the number of web pages on the Internet is too large. In this paper, we propose the way to construct a new type of web page recommender system covering all over the Internet, by using Folksonomy and Social Bookmark which are getting very popular in these days.}, biburl = {http://www.bibsonomy.org/bibtex/2b1cb4183d3ad183709ed11780f1b5fdf/wnpxrz}, keywords = {web tagging filtering tags recommendersystems imported collaborative folksonomy} } @article{keyhere, title = {Cubic Analysis of Social Bookmarking for Personalized Recommendation}, author = {Yanfei Xu and Liang Zhang and Wei Liu}, journal = {Frontiers of WWW Research and Development - APWeb 2006}, pages = {733--738}, year = 2006, url = {http://dx.doi.org/10.1007/11610113_66}, description = {SpringerLink - Book Chapter}, 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 -}, biburl = {http://www.bibsonomy.org/bibtex/25fbd24f07fe8784b516e69b0eb3192f3/wnpxrz}, keywords = {bookmarking social filtering collaborative tagging recommendersystems} } @inproceedings{Byde2007, title = {Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics}, author = {Andrew Byde and Hui Wan and Steve Cayzer}, booktitle = {Proceedings of the International Conference on Weblogs and Social Media}, month = {March}, year = 2007, url = {http://www.icwsm.org/papers/paper47.html}, priority = {5}, 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.}, biburl = {http://www.bibsonomy.org/bibtex/2157846898c1c2a65c265a913ebac115a/wnpxrz}, keywords = {content tagging tags tag recommendersystems} } @article{keyhere, title = {Content-Based Recommendation Systems}, author = {Michael Pazzani and Daniel Billsus}, journal = {The Adaptive Web}, pages = {325--341}, year = 2007, url = {http://dx.doi.org/10.1007/978-3-540-72079-9_10}, description = {SpringerLink - Book Chapter}, abstract = {This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user’s interests. Content-based recommendation systems may be used in a variety of domainsranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the detailsof various systems differ, content-based recommendation systems share in common a means for describing the items that maybe recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means ofcomparing items to the user profile to determine what to re commend. The profile is often created and updated automaticallyin response to feedback on the desirability of items that have been presented to the user.}, biburl = {http://www.bibsonomy.org/bibtex/2df9418325ea7a6a3258748d498241812/wnpxrz}, keywords = {recommendersystems conntent imported} } @inproceedings{1125659, title = {Being accurate is not enough: how accuracy metrics have hurt recommender systems}, address = {New York, NY, USA}, author = {Sean M. McNee and John Riedl and Joseph A. Konstan}, booktitle = {CHI '06: CHI '06 extended abstracts on Human factors in computing systems}, pages = {1097--1101}, publisher = {ACM}, year = 2006, url = {http://portal.acm.org/citation.cfm?id=1125451.1125659#}, location = {Montr\&\#233;al, Qu\&\#233;bec, Canada}, isbn = {1-59593-298-4}, doi = {http://doi.acm.org/10.1145/1125451.1125659}, description = {Being accurate is not enough}, abstract = {Recommender systems have shown great potential to help users find interesting and relevant items from within a large information space. Most research up to this point has focused on improving the accuracy of recommender systems. We believe that not only has this narrow focus been misguided, but has even been detrimental to the field. The recommendations that are most accurate according to the standard metrics are sometimes not the recommendations that are most useful to users. In this paper, we propose informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies. We propose new user-centric directions for evaluating recommender systems.}, biburl = {http://www.bibsonomy.org/bibtex/296968eb56f0a3b40f7bc008477088309/wnpxrz}, keywords = {recommendersystems evaluation imported} } @misc{zimmerman02exposing, title = {Exposing profiles to build trust in a recommender}, author = {J. Zimmerman and K. Kurapati}, year = 2002, url = {citeseer.ist.psu.edu/article/zimmerman02exposing.html}, description = {Exposing Profiles to Build Trust in a Recommender - Zimmerman, Kurapati (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/210c004805cae485e8f5d90442285fbeb/wnpxrz}, keywords = {recommendersystems trust imported profile} } @inproceedings{good99combining, title = {Combining Collaborative Filtering with Personal Agents for Better Recommendations}, author = {Nathaniel Good and J. Ben Schafer and Joseph A. Konstan and Al Borchers and Badrul M. Sarwar and Jonathan L. Herlocker and John Riedl}, booktitle = {{AAAI}/{IAAI}}, pages = {439-446}, year = 1999, url = {citeseer.ist.psu.edu/good99combining.html}, description = {Combining Collaborative Filtering with Personal Agents for Better Recommendations - Good, Schafer, Konstan, Borchers, Sarwar, Herlocker, Riedl (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/2b92814aa0b942be645ec0955b360e4ce/wnpxrz}, keywords = {recommendersystems agent personal filtering imported collaborative} } @article{citeulike:206218, title = {Recommender Systems Research: A Connection-Centric Survey}, author = {Saverio Perugini and Marcos A. Gonã§alves and Edward A. Fox}, journal = {Journal of Intelligent Information Systems}, number = 2, pages = {107--143}, volume = 23, year = 2004, url = {http://www.metapress.com/link.asp?id=R322040289831534}, id = {206218}, priority = {2}, description = {CiteULike: Recommender Systems Research: A Connection-Centric Survey}, biburl = {http://www.bibsonomy.org/bibtex/2679137d129c0dd5d60d69f15b99adf42/wnpxrz}, keywords = {tostartwith filtering recommendersystems collaborative survey} } @article{245122, title = {PHOAKS: a system for sharing recommendations}, address = {New York, NY, USA}, author = {Loren Terveen and Will Hill and Brian Amento and David McDonald and Josh Creter}, journal = {Commun. ACM}, number = 3, pages = {59--62}, publisher = {ACM Press}, volume = 40, year = 1997, url = {http://portal.acm.org/citation.cfm?id=245122}, issn = {0001-0782}, doi = {http://doi.acm.org/10.1145/245108.245122}, description = {PHOAKS}, biburl = {http://www.bibsonomy.org/bibtex/220c2a861b55360b2d0a2481b54b7e657/wnpxrz}, keywords = {recommendersystems sharing imported} } @inproceedings{500755, title = {Capturing knowledge of user preferences: ontologies in recommender systems}, address = {New York, NY, USA}, author = {Stuart E. Middleton and David C. De Roure and Nigel R. Shadbolt}, booktitle = {K-CAP '01: Proceedings of the 1st international conference on Knowledge capture}, pages = {100--107}, publisher = {ACM Press}, year = 2001, url = {http://portal.acm.org/citation.cfm?id=500755}, location = {Victoria, British Columbia, Canada}, isbn = {1-58113-380-4}, doi = {http://doi.acm.org/10.1145/500737.500755}, description = {Capturing knowledge of user preferences}, abstract = {Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.}, biburl = {http://www.bibsonomy.org/bibtex/26d0a7792db2c0f96bd0a495a56e57464/wnpxrz}, keywords = {preference ontology user imported recommendersystems} } @book{citeulike:224398, title = {Using Trust in Recommender Systems: An Experimental Analysis}, author = {Paolo Massa and Bobby Bhattacharjee}, journal = {Lecture Notes in Computer Science}, month = {February}, pages = {221--235}, volume = 2995, year = 2004, url = {http://www.metapress.com/link.asp?id=TFCG7W34VF58YAWL}, id = {224398}, priority = {0}, comment = {Abstract Recommender systems (RS) have been used for suggesting items (movies, books, songs, etc.) that users might like. RSs compute a user similarity between users and use it as a weight for the usersrsquo ratings. However they have many weaknesses, such as sparseness, cold start and vulnerability to attacks. We assert that these weaknesses can be alleviated using a Trust-aware system that takes into account the ldquoweb of trustrdquo provided by every user. Specifically, we analyze data from the popular Internet web site epinions.com. The dataset consists of 49290 users who expressed reviews (with rating) on items and explicitly specified their web of trust, i.e. users whose reviews they have consistently found to be valuable. We show that any two users have usually few items rated in common. For this reason, the classic RS technique is often ineffective and is not able to compute a user similarity weight for many of the users. Instead exploiting the webs of trust, it is possible to propagate trust and infer an additional weight for other users. We show how this quantity can be computed against a larger number of users. }, description = {CiteULike: Using Trust in Recommender Systems: An Experimental Analysis}, biburl = {http://www.bibsonomy.org/bibtex/2aa45e45bc1ace8cee5b52d7b62459325/wnpxrz}, keywords = {collaborative filtering trust recommendersystems} }