BibSonomy publications for /user/wnpxrz/recommendersystemsMon Apr 07 16:20:08 CEST 2008Proceedings of the 15th international conference on World Wide Web1039--1040{Mining search engine query logs for query recommendation}2006recommendation recommendersystems query log search ir Thu Jan 24 17:56:24 CET 2008Semantic Web Workshopconf/sww/2002CEUR Workshop ProceedingsExploiting Synergy Between Ontologies and Recommender Systems.552002recommendersystems ontology Thu Jan 24 17:47:05 CET 2008New York, NY, USAACM Trans. Inf. Syst.15--53Evaluating collaborative filtering recommender systems222004recommendersystems evaluation collaborative filtering 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.Thu Jan 03 17:33:51 CET 2008Natural Language Processing and Information Systems48--60Exploit Semantic Information for Category Annotation Recommendation in Wikipedia2007wikipedia recommendersystems imported 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.Sun Dec 30 11:43:14 CET 2007Beyond Recommender Systems: Helping People Help Each Other2001collaborative recommendersystems imported filtering Wed Nov 21 16:13:33 CET 2007Data Mining and Knowledge Discovery83--105Efficient adaptive-support association rule mining for recommender systems62002recommendersystems rule mining Wed Nov 21 16:05:02 CET 2007Proceedings of I-Semantics' 07pp. 297-304ConTag: A semantic tag recommendation system2007tags tagging recommendersystems 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.Wed Nov 21 16:03:11 CET 2007Proceedings of the SIAM International Conference on Data Mining{Influence in ratings-based recommender systems: An algorithm-independent approach}2005recommendersystems Wed Nov 21 16:02:57 CET 2007Menlo Park, CA, USAAAAI '98/IAAI '98: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence714--720Recommendation as classification: using social and content-based information in recommendation1998social filtering classification content collaborative recommendersystems Mon Nov 19 15:49:43 CET 2007Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on388- 393Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions2006recommendersystems tags collaborative tagging filtering folksonomy web imported 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.Mon Nov 19 15:48:13 CET 2007Frontiers of WWW Research and Development - APWeb 2006733--738Cubic Analysis of Social Bookmarking for Personalized Recommendation2006tagging social collaborative bookmarking recommendersystems filtering 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 -Mon Nov 19 15:46:45 CET 2007Proceedings of the International Conference on Weblogs and Social MediaMarchPersonalized Tag Recommendations via Tagging and Content-based Similarity Metrics2007content tagging recommendersystems tag tags 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.Tue Nov 06 16:57:41 CET 2007The Adaptive Web325--341Content-Based Recommendation Systems2007recommendersystems conntent imported 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.Sun Nov 04 16:12:11 CET 2007New York, NY, USACHI '06: CHI '06 extended abstracts on Human factors in computing systems1097--1101Being accurate is not enough: how accuracy metrics have hurt recommender systems2006imported recommendersystems evaluation 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.Sun Nov 04 09:12:31 CET 2007Exposing profiles to build trust in a recommender2002imported recommendersystems profile trust Wed Oct 24 21:30:12 CEST 2007{AAAI}/{IAAI}439-446Combining Collaborative Filtering with Personal Agents for Better Recommendations1999imported recommendersystems filtering collaborative agent personal Fri Oct 19 20:39:21 CEST 2007Journal of Intelligent Information Systems2107--143Recommender Systems Research: A Connection-Centric Survey232004recommendersystems collaborative tostartwith survey filtering Fri Oct 19 20:38:21 CEST 2007New York, NY, USACommun. ACM359--62PHOAKS: a system for sharing recommendations401997recommendersystems imported sharing Fri Oct 19 20:35:47 CEST 2007New York, NY, USAK-CAP '01: Proceedings of the 1st international conference on Knowledge capture100--107Capturing knowledge of user preferences: ontologies in recommender systems2001user imported ontology recommendersystems preference 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.Fri Oct 19 20:34:27 CEST 2007Lecture Notes in Computer ScienceFebruary221--235Using Trust in Recommender Systems: An Experimental Analysis29952004trust collaborative filtering recommendersystems