Alternating least squares for personalized ranking

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

Volumn , Issue , 2012, Pages 83-90

  Takács, Gábor ^a   Tikk, Domonkos ^b  

^a SZÉCHENYI ISTVÁN UNIVERSITY   (Hungary)

^b Gravity Research and Development Inc   (Hungary)

Author keywords

Alternating least squares; Collaborative filtering; Ranking

Indexed keywords

ALTERNATING LEAST SQUARES; COLLABORATIVE FILTERING; DATA SETS; DIRECT MINIMIZATION; EVALUATION METRICS; EXPLICIT FEEDBACK; IMPLICIT FEEDBACK; OBJECTIVE FUNCTIONS; PREDICTION MEAN-SQUARED ERRORS; PREFERENCE RELATIONSHIPS; RANKING;

RECOMMENDER SYSTEMS;

EID: 84867341601     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2365952.2365972     Document Type: Conference Paper

References (10)

1. G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17:734-749, 2005. (Pubitemid 40860454)
2. th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD '07, pages 3-6, San Jose, CA, USA, 2007.
3. G. Dror, N. Koenigstein, Y. Koren, and M. Weimer. The Yahoo! Music dataset and KDD-Cup'11. In Proc. of KDD Cup 2011, 2011.
4. th IEEE Int. Conf. on Data Mining, ICDM '08, pages 263-272, Pisa, Italy, 2008.
5. M. Jahrer and A. Töscher. Collaborative filtering ensemble for ranking. In Proc. of KDD Cup Workshop at 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD '11, San Diego, CA, USA, 2011.
6. th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD '11, San Diego, CA, USA, 2011.
7. R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. M. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. In Proc. of 8th IEEE Int. Conf. on Data Mining, ICDM '08, pages 502-511, Pisa, Italy, 2008.
8. I. Pilászy, D. Zibriczky, and D. Tikk. Fast ALS-based matrix factorization for explicit and implicit feedback datasets. In Proc. of the 4th ACM conference on Recommender Systems, RecSys '10, pages 71-78, Barcelona, Spain, 2010.
9. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proc. of the 25th Conf. on Uncertainty in Artificial Intelligence, UAI '09, pages 452-461, 2009.
10. X. Su and T. M. Khoshgoftaar. A survey of collaborative filtering techniques. Advances in Artificial Intellegence, 2009, 2009. Article ID: 421425, 19 pages.