Various aspects of user preference learning and recommender systems

CEUR Workshop Proceedings

Volumn 471, Issue , 2009, Pages 56-67

  Eckhardt, Alan ^a  

^a CHARLES UNIVERSITY   (Czech Republic)

Author keywords

Data mining; Recommender systems; User preference learning

Indexed keywords

PREFERENCE LEARNING;

DATA MINING; SPECIFICATIONS;

RECOMMENDER SYSTEMS;

EID: 84868702662     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. The internet movie database. http://www.imdb.com/.
2. Movielens dataset. http://www.grouplens.org/node/73.
3. Netflix dataset. http://www.netflixprize.com.
4. Preference handling an introductory tutorial. http://www.cs.bgu.ac.il/ brafman/tutorial.pdf.
5. Improving Recommendation Lists Through Topic Diversification, 2005.
6. R. Agrawal and E. L. Wimmers. A framework for expressing and combining preferences. SIGMOD Rec., 29(2):297-306, 2000.
7. C. Boutilier, R. I. Brafman, H. H. Hoos, and D. Poole. Cp-nets: A tool for representing and reasoning with conditional ceteris paribus preference statements. Journal of Artificial Intelligence Research, 21:2004, 2004.
8. J. Chomicki. Preference queries. CoRR, cs.DB/0207093, 2002.
9. J. Chomicki. Preference formulas in relational queries. ACM Trans. Database Syst., 28(4):427-466, 2003.
10. J. Doyle. Prospects for preferences. Computational Intelligence, 20:111-136(26), May 2004.
11. A. Eckhardt. Inductive models of user preferences for semantic web. In J. Pokorný, V. Snášel, and K. Richta, editors, DATESO 2007, volume 235 of CEUR Workshop Proceedings, pages 108-119. Matfyz Press, Praha, 2007.
12. A. Eckhardt, T. Horváth, D. Maruščák, R. Novotný, and P. Vojtáš. Uncertainty issues in automating process connecting web and user. In P. C. G. da Costa, editor, URSW '07 Uncertainty Reasoning for the Semantic Web - Volume 3, pages 97-108. The 6th International Semantic Web Conference, 2007.
13. A. Eckhardt, T. Horváth, and P. Vojtáš. Learning different user profile annotated rules for fuzzy preference top-k querying. In H. Prade and V. Subrahmanian, editors, International Conference on Scalable Uncertainty Management, volume 4772 of Lecture Notes In Computer Science, pages 116-130, Washington DC, USA, 2007. Springer Berlin /Heidelberg.
14. A. Eckhardt, T. Horváth, and P. Vojtáš. PHASES: A user profile learning approach for web search. In T. Lin, L. Haas, R. Motwani, A. Broder, and H. Ho, editors, 2007 IEEE/WIC/ACM International Conference on Web Intelligence - WI 2007, pages 780-783. IEEE, 2007.
15. A. Eckhardt and P. Vojtáš. Considering data-mining techniques in user preference learning. In 2008 International Workshop on Web Information Retrieval Support Systems, 2008.
16. R. Fagin. Combining fuzzy information from multiple systems. pages 216-226, 1996.
17. J. Fürnkranz and E. Hüllermeier. Pairwise preference learning and ranking. In In Proc. ECML-03, Cavtat-Dubrovnik, pages 145-156. Springer-Verlag, 2003.
18. D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Commun. ACM, 35(12):61-70, 1992.
19. S. Holland, M. Ester, and W. Kiessling. Preference mining: A novel approach on mining user preferences for personalized applications. In Knowledge Discovery in Databases: PKDD 2003, pages 204-216. Springer Berlin /Heidelberg, 2003.
20. E. Hüllermeier and J. Fürnkranz. Learning preference models from data: On the problem of label ranking and its variants. In G. D. Riccia, D. Dubois, R. Kruse, and H. Lenz, editors, Preferences and Similarities, pages 283-304. Springer-Verlag, 2008.
21. T. Joachims. Optimizing search engines using clickthrough data. In KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 133-142, New York, NY, USA, 2002. ACM Press.
22. T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting click-through data as implicit feedback. In SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 154-161, New York, NY, USA, 2005. ACM.
23. G. K. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 4:133-151(19), July 2001.
24. W. Kiessling. Foundations of preferences in database systems. In VLDB '02: Proceedings of the 28th international conference on Very Large Data Bases, pages 311-322. VLDB Endowment, 2002.
25. W. Kiessling and G. Köstler. Preference sql: design, implementation, experiences. In VLDB '02: Proceedings of the 28th international conference on Very Large Data Bases, pages 990-1001. VLDB Endowment, 2002.
26. S. E. Middleton, N. Shadbolt, and D. D. Roure. Capturing interest through inference and visualization: Ontological user profiling in recommender systems. In K-CAP2003, 2003.
27. A. M. Rashid, I. Albert, D. Cosley, S. K. Lam, S. M. McNee, J. A. Konstan, and J. Riedl. Getting to know you: learning new user preferences in recommender systems. In IUI '02: Proceedings of the 7th international conference on Intelligent user interfaces, pages 127-134, New York, NY, USA, 2002. ACM.
28. G. H. Wright. The logic of preference reconsidered. In Theory and Decision, volume 3, pages 140-169, 1972.