BibSonomy publications for /user/krass/recommenderTue Apr 01 13:03:42 CEST 2008Artificial Intelligence Review285-330A Taxonomy of Recommender Agents on the Internet192003systems recommender Sun Nov 25 14:18:15 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 recommendation1998recommender Sun Nov 25 14:18:02 CET 2007San Francisco, CA, USAICML '01: Proceedings of the Eighteenth International Conference on Machine Learning314--321Collaborative Learning and Recommender Systems2001recommender collaborative Sun Nov 25 14:16:50 CET 2007ISMISconf/ismis/2005553-561Lecture Notes in Computer ScienceIncremental Collaborative Filtering for Highly-Scalable Recommendation Algorithms.34882005collaborative recommender Sat Nov 17 12:24:44 CET 2007Proceedings of the International Conference on Weblogs and Social MediaMarchPersonalized Tag Recommendations via Tagging and Content-based Similarity Metrics2007recommender 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 13 16:07:39 CET 2007Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep13-20Tag Recommendations in Folksonomies2007recommender folksonomies Tue Nov 06 09:50:07 CET 2007Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, ProceedingsDBLP:conf/pkdd/2007506-514Lecture Notes in Computer ScienceTag Recommendations in Folksonomies47022007recommender Sun Nov 04 17:36:46 CET 2007ICCBRconf/iccbr/2007299-313Lecture Notes in Computer ScienceCase-Based Group Recommendation: Compromising for Success.46262007cbr recommender Fri Nov 02 17:11:10 CET 2007Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep13-20Tag Recommendations in Folksonomies2007recommender Thu Nov 01 21:57:22 CET 2007New York, NY, USAIUI '06: Proceedings of the 11th international conference on Intelligent user interfaces267--269Group recommender systems: a critiquing based approach2006recommender Group recommender systems introduce a whole set of new challenges for recommender systems research. The notion of generating a set of recommendations that will satisfy a group of users, with potentially competing interests, is challenging in itself. In addition to this we must consider how to record and combine the preferences of many different users as they engage in simultaneous recommendation dialogs. In this paper we introduce a group recommender system that is designed to provide assistance to a group of friends trying to plan a skiing vacation.Thu Nov 01 14:22:12 CET 2007IWP-Information Wissenschaft & Praxis5265-276Empfehlungssysteme aus informationswissenschaftlicher Sicht-State of the Art582007recommender 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.Thu Nov 01 12:39:18 CET 2007Houston, Texas, USAProceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27-30 November 2005378-385Compound Classification Models for Recommender Systems.2005recommender Thu Nov 01 12:39:06 CET 2007Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, ProceedingsDBLP:conf/pkdd/2007506-514Lecture Notes in Computer ScienceTag Recommendations in Folksonomies47022007recommender folksonomy Wed Oct 31 13:50:24 CET 2007Proceedings of the 10th International Conference on Intelligent User Interfaces167--174Trust in recommender systems2005recommender Wed Oct 31 13:49:52 CET 2007The Adaptive Web: Methods and Strategies of Web Personalization, Lecture Notes in Computer Science, Vol. 4321291--324Collaborative Filtering Recommender Systems 2006recommender Thu Oct 25 16:52:15 CEST 2007{AAAI}/{IAAI}439-446Combining Collaborative Filtering with Personal Agents for Better Recommendations1999recommender collaborative_filtering Tue Oct 23 09:29:53 CEST 2007New York, NY, USAWikiSym '07: Proceedings of the 2007 international symposium on Wikis47--60Recommenders everywhere:: the WikiLens community-maintained recommender system2007recommender Mon Oct 22 14:47:44 CEST 2007Proc. of the 4th International Semantic Web ConferenceSemantically rich recommendations in social networks for sharing, exchanging and ranking semantic context2005recommender Sat Oct 20 12:08:07 CEST 2007Journal of Intelligent Information Systems2107--143Recommender Systems Research: A Connection-Centric Survey232004survey recommender Sat Oct 20 12:07:47 CEST 2007New York, NY, USACommun. ACM359--62PHOAKS: a system for sharing recommendations401997recommender