Fast online learning through offline initialization for time-sensitive recommendation

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2010, Pages 703-712

  Agarwal, Deepak ^a   Chen, Bee Chung ^a   Elango, Pradheep ^a  

^a YAHOO RESEARCH   (United States)

Author keywords

Dyadic data; Factorization; Latent factor; Recommender systems; Reduced rank regression

Indexed keywords

DATA SETS; DYADIC DATA; FACTOR MODEL; HIGH DIMENSIONALITY; HISTORICAL DATA; ITEM-LEVEL; LATENT FACTOR; LINEAR PROJECTIONS; LOG LIKELIHOOD; NEWS ARTICLES; NOVEL METHODS; OFFLINE; ON-LINE FASHION; ONLINE ADVERTISING; ONLINE LEARNING; ONLINE MODELS; RANK CORRELATION; RECOMMENDER SYSTEMS; REDUCED RANK REGRESSION; ROOT MEAN SQUARED ERRORS; WEB APPLICATION; WEB SEARCHES;

FACTORIZATION; OPTIMIZATION; REGRESSION ANALYSIS;

WORLD WIDE WEB;

EID: 77956208406     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1835804.1835894     Document Type: Conference Paper

References (26)

1. D. Agarwal and B.-C. Chen. Regression-based latent factor models. In Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2009.
2. D. Agarwal, B.-C. Chen, P. Elango, and et al. Online models for content optimization. In Proceedings of the 22nd Annual Conference on Neural Information Processing Systems (NIPS), 2008.
3. J.-W. Ahn, P. Brusilovsky, J. Grady, D. He, and S. Y. Syn. Open user profiles for adaptive news systems: help or harm? In Proceedings of the 16th international conference on World Wide Web (WWW), 2007.
4. T. Anderson. Estimating linear restrictions on regression coefficients for multivariate normal distributions. Annals of Mathematical Statsitics, 22:327-351, 1951.
5. R. Bell, Y. Koren, and C. Volinsky. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In Proceedings of the 13rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2007.
6. D. Billsus and M. Pazanni. Springer, Berlin, 2007.
7. J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative. In Proceedings of the 4th Conference on Uncertainty in Artificial Intelligence (UAI), 1998.
8. M. Brezzi and T. Lai. Optimal learning and experimentation in bandit problems. journal of economic dynamics and control, 27(1):87-108, 2001.
9. A. Broder. Computational advertising and recommender systems. In Proceedings of the 2nd ACM International Conference on Recommender Systems (RecSys), 2008.
10. G. Celeux and G. Govaert. A classification em algorithm for clustering and two stochastic versions. Computational Statistics and Data Analysis, 14:315-332, 1992.
11. D. Chakrabarti, D. Agarwal, and V. Josifovski. Contextual advertising by combining relevance with click feedback. In Proceedings of the 17th international conference on World Wide Web (WWW), 2008.
12. Y. Chen, D. Pavlov, and J. F. Canny. Large-scale behavioral targeting. In Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2009.
13. W. Chu and S. T. Park. Personalized recommendation on dynamic content using predictive bilinear models. In Proceedings of the 18th international conference on World Wide Web (WWW), 2009.
14. G. C. Reinsel and R. Velu. Multivariate Reduced-Rank Regression: Theory and Applications. Springer: New York, 1998.
15. A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web (WWW), 2007.
16. M. Kendall. A new measure of rank correlation. Biometrika, 1938.
17. Y. Koren. Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2009.
18. C.-J. Lin, R. C. Weng, and S. S. Keerthi. Trust region newton methods for large-scale logistic regression. In Proceedings of the 24th International Conference on Machine Learning (ICML), 2007.
19. B. Marx. Iteratively reweighted partial least squares estimation for generalized linear regression. Technometrics, 38(4):374-381, 1996.
20. P. McCullagh and J. A. Nelder. Generalized Linear Models. Chapman & Hall/CRC, 1989.
21. A. Micarelli, F. Gasparetti, F. Sciarrone, and S. Gauch. Personalized search on the world wide web. The Adaptive Web, pages 195-230, 2007.
22. M. West and J. Harrison. Bayesian Forecasting and Dynamic Models. 1997.
23. M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web (WWW), 2007.
24. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Proceedings of the 22nd Annual Conference on Neural Information Processing Systems (NIPS), 2008.
25. M. West. Bayesian factor regression models in the "large p, small n" paradigm. Bayesian Statistics, 7:723-732, 2003.
26. Z. Lu, D. Agarwal, and I. Dhillon. A spatio-temporal approach to collaborative filtering. In Proceedings of the 3rd ACM International Conference on Recommender Systems (RecSys), 2009.