Effective latent models for binary feedback in recommender systems

SIGIR 2015 - Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2015, Pages 313-322

  Volkovs, Maksims N ^a   Yu, Guang Wei ^a  

^a Milq Inc   (Canada)

Author keywords

Binary feedback; Collaborative filtering; Latent models

Indexed keywords

BINS; FACTORIZATION; FEEDBACK; INFORMATION RETRIEVAL; RECOMMENDER SYSTEMS;

BINARY FEEDBACK; FACTORIZATION METHODS; IMPLICIT FEEDBACK; LATENT MODELS; MODEL CHOICE; ORDERS OF MAGNITUDE; SIMILARITY INFORMATIONS; STATE OF THE ART;

COLLABORATIVE FILTERING;

EID: 84953791801     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2766462.2767716     Document Type: Conference Paper

References (25)

1. P.-A. Absil, C. G. Baker, and K. A. Gallivan. Trust-region methods on riemannian manifolds. Foundations of Computational Mathematics, 7(3), 2007.
2. 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(6), 2005.
3. F. Aiolli. Efficient top-n recommendation for very large scale binary rated datasets. In ACM Recommender Systems, 2013.
4. R. Baeza-Yates and B. Ribeiro-Neto. Information Retrieval. Addison-Wesley, 1999.
5. J. Bennet and S. Lanning. The Netix prize. www.cs.uic.edu/~liub/KDD-cup-2007/NetflixPrize-description.pdf.
6. D. Billsus and M. J. Pazzani. Learning collaborative information filters. In International Conference on Machine Learning, 1998.
7. M. Brand. Fast low-rank modifications of the thin singular value decomposition. Linear algebra and its applications, 415(1), 2006.
8. C. J. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In International Conference on Machine Learning, 2005.
9. Z. Cao, T. Qin, T.-Y. Liu, M.-F. Tsai, and H. Li. Learning to rank: From pairwise approach to listwise approach. In International Conference on Machine Learning, 2007.
10. Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. MyMediaLite: A free recommender system library. In ACM Recommender Systems, 2011.
11. N. Halko, P. G. Martinsson, and J. A. Tropp. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. SIAM Review, 53(2), 2011.
12. J. Herlocker, J. A. Konstan, and J. Riedl. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information Retrieval, 5(4), 2002.
13. Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In International Conference on Data Engineering, 2008.
14. Z. Huang, D. Zeng, and H. Chen. A comparison of collaborative-filtering recommendation algorithms for e-commerce. IEEE Intelligent Systems, 22(5), 2007.
15. K. Jarvelin and J. Kekalainen. IR evaluation methods for retrieving highly relevant documents. In ACM Special Interest Group on Information Retrieval, 2000.
16. T.-Y. Liu. Learning to rank for information retrieval. Springer, 2011.
17. Apache Software Foundation. Mahout stochastic singular value decomposition. https://mahout.apache.org/users/dim-reduction/dimensional-reduction.html.
18. B. McFee, T. Bertin-Mahieux, D. P. Ellis, and G. R. Lanckriet. The Million Song Dataset Challenge. http://www.kaggle.com/c/msdchallenge. In International World Wide Web Conference, 2012.
19. H. Polat and W. Du. SVD-based collaborative filtering with privacy. In ACM Symposium On Applied Computing, 2005.
20. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In Conference on Uncertainty in Artificial Intelligence, 2009.
21. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Neural Information Processing Systems, 2008.
22. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Application of dimensionality reduction in recommender system - a case study. Technical report, DTIC Document, 2000.
23. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In International World Wide Web Conference, 2001.
24. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Incremental singular value decomposition algorithms for highly scalable recommender systems. In International Conference on Information Systems, 2002.
25. R. Yuster and U. Zwick. Fast sparse matrix multiplication. ACM Transactions on Algorithms, 1(1), 2005.