Personalized implicit learning in a music recommender system

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6075 LNCS, Issue , 2010, Pages 351-362

  Kordumova, Suzana ^a   Kostadinovska, Ivana ^a   Barbieri, Mauro ^b   Pronk, Verus ^b   Korst, Jan ^b  

^a SS CYRIL AND METHODIUS UNIVERSITY   (Macedonia)

^b PHILIPS RESEARCH LABORATORIES   (Netherlands)

Author keywords

implicit learning; naive Bayesian classification; recommender systems; relief algorithm; user behavior

Indexed keywords

IMPLICIT LEARNING; NAIVE BAYESIAN CLASSIFICATION; RECOMMENDER SYSTEMS; RELIEF ALGORITHM; USER BEHAVIORS;

BAYESIAN NETWORKS; BEHAVIORAL RESEARCH;

LEARNING ALGORITHMS;

EID: 77954582637     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13470-8_32     Document Type: Conference Paper

References (11)

1. MovieLens Home Page, http://www.movielens.org/
2. TiVo Home Page, http://www.tivo.com/
3. Watchmi Home Page, http://www.watchmi.tv/
4. Oh, J., Lee, S., Lee, E.: A user modeling using implicit feedback for effective recommender system. In: ICHIT '08: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology, pp. 155-158. IEEE Computer Society, Washington (2008)
5. Park, Y.-J., Chang, K.-N.: Individual and group behavior-based customer profile model for personalized product recommendation. Expert Syst. Appl. 36(2), 1932-1939 (2009)
6. Qiu, F., Cho, J.: Automatic identification of user interest for personalized search. In: WWW '06: Proceedings of the 15th international conference on World Wide Web, pp. 727-736. ACM, New York (2006)
7. Gadanho, S.: TV Recommendations based on implicit information. In: ECAI'06 workshop on Recommender Systems (2006)
8. Pronk, V., Verhaegh, W., Proidl, A., Tiemann, M.: Incorporating User Control into Recommender Systems Based on Naive Bayesian Classification. In: RecSys 2007: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 73-80. ACM, Minneapolis (2007)
9. Tiemann, M., Pauws, S., Vignoli, F.: A Modular Hybrid Recommender System. In: BNAIC 2009: Proceedings of the 21st Conference on Artificial Intelligence, Eindhoven, The Netherlands (2009)
10. Kira, K., Rendell, L.A.: The feature selection problem: Traditional methods and a new algorithm. In: AAAI, pp. 129-134. AAAI Press and MIT Press, Cambridge (1992)
11. Hanneke, S.: Theoretical Foundations of Active Learning, Ph.D. thesis, Carnegie Mellon University (2009)