Local implicit feedback mining for music recommendation

RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems

Volumn , Issue , 2012, Pages 91-98

  Yang, Diyi ^a   Chen, Tianqi ^a   Zhang, Weinan ^a   Lu, Qiuxia ^a   Yu, Yong ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Collaborative filtering; Efficient training; Local implicit feedback; Recommender system

Indexed keywords

BASELINE MODELS; COLLABORATIVE FILTERING; DIGITAL MUSIC; GLOBAL BEHAVIORS; IMPLICIT FEEDBACK; MUSIC COLLECTION; MUSIC RECOMMENDATION; RECOMMENDATION PERFORMANCE; TEMPORAL MODELS; TIME GRANULARITIES; TRAINING ALGORITHMS;

RECOMMENDER SYSTEMS;

EID: 84867382271     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2365952.2365973     Document Type: Conference Paper

References (26)

1. G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. Recommender Systems Handbook, pages 217-253, 2011.
2. E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 19-26. ACM, 2006.
3. R. Andersen, C. Borgs, J. Chayes, U. Feige, A. Flaxman, A. Kalai, V. Mirrokni, and M. Tennenholtz. Trust-based recommendation systems: an axiomatic approach. In Proceeding of the 17th international conference on World Wide Web, pages 199-208. ACM, 2008.
4. O. Celma. Music Recommendation and Discovery in the Long Tail. Springer, 2010.
5. T. Chen, Z. Zheng, Q. Lu, X. Jiang, Y. Chen, W. Zhang, K. Chen, Y. Yu, N. Liu, B. Cao, L. He, and Q. Yang. Informative ensemble of multi-resolution dynamic factorization models. In KDD-Cup Workshop, 2011.
6. P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pages 39-46, New York, NY, USA, 2010. ACM.
7. Y. Ding and X. Li. Time weight collaborative filtering. In Proceedings of the 14th ACM international conference on Information and knowledge management, pages 485-492. ACM, 2005. (Pubitemid 44022221)
8. A. Ferman, J. Errico, P. Beek, and M. Sezan. Content-based filtering and personalization using structured metadata. In Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries, pages 393-393. ACM, 2002.
9. B. D. G.Takacs, I.Pilaszy. A unified approach of factor models and neighbor based methods for large recommender systems. In In Proc. of ICADIWT-08, 1st IEEE Workshop on Recommender Systems and Personalized Retrieval, pages 186-191, 2008.
10. Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE International Conference on Data Mining, pages 263-272. IEEE, 2008.
11. T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 154-161. ACM, 2005.
12. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '08, pages 426-434, New York, NY, USA, 2008. ACM.
13. Y. Koren. Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pages 447-456, 2009.
14. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42, August 2009.
15. N. Lathia, S. Hailes, and L. Capra. Temporal collaborative filtering with adaptive neighbourhoods. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pages 796-797. ACM, 2009.
16. B. Liu. Web data mining: exploring hyperlinks, contents, and usage data (Second Edition). Springer Verlag, 2011.
17. N. N. Liu and Q. Yang. Eigenrank: a ranking-oriented approach to collaborative filtering. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pages 83-90, New York, NY, USA, 2008. ACM.
18. J. R. Lorraine McGinty. On the Evolution of Critiquing Recommenders. Springer, 2011.
19. M. Park, J. Hong, and S. Cho. Location-based recommendation system using bayesian usera̧ŕs preference model in mobile devices. Ubiquitous Intelligence and Computing, pages 1130-1139, 2007.
20. S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web, WWW '10, pages 811-820, New York, NY, USA, 2010. ACM.
21. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, WWW '01, pages 285-295, New York, NY, USA, 2001. ACM.
22. X. Shen, B. Tan, and C. Zhai. Context-sensitive information retrieval using implicit feedback. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 43-50. ACM, 2005.
23. Y. Shi, M. Larson, and A. Hanjalic. Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering. In Proceedings of the third ACM conference on Recommender systems, RecSys '09, pages 125-132, New York, NY, USA, 2009. ACM.
24. Y. Shi, M. Larson, and A. Hanjalic. Mining mood-specific movie similarity with matrix factorization for context-aware recommendation. In Proceedings of the Workshop on Context-Aware Movie Recommendation, pages 34-40. ACM, 2010.
25. R. Van Meteren and M. Van Someren. Using content-based filtering for recommendation. In Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, 2000.
26. L. Xiang, Q. Yuan, S. Zhao, L. Chen, X. Zhang, Q. Yang, and J. Sun. Temporal recommendation on graphs via long-and short-term preference fusion. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 723-732. ACM, 2010.