Contextual collaborative filtering via hierarchical matrix factorization

Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012

Volumn , Issue , 2012, Pages 744-755

  Zhong, Erheng ^a   Fan, Wei ^b   Yang, Qiang ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COLLABORATIVE FILTERING; DATA MINING; FACTORIZATION; SPACE DIVISION MULTIPLE ACCESS;

COLLABORATIVE INFORMATION; CONTEXTUAL INFORMATION; FACTORIZATION MODEL; HETEROGENEOUS ENVIRONMENTS; HIERARCHICAL MATRIX FACTORIZATIONS; MATRIX FACTORIZATIONS; RECOMMENDATION ACCURACY; STATE-OF-THE-ART ALGORITHMS;

MATRIX ALGEBRA;

EID: 84880221335     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972825.64     Document Type: Conference Paper

References (25)

1. Bin Cao, Nathan Nan Liu, and Qiang Yang. Transfer learning for collective link prediction in multiple heterogenous domains. In Johannes Fürnkranz and Thorsten Joachims, editors, Proceedings of the 27th International Conference on Machine Learning, ICML '10, pages 159-166, Haifa, Israel, 2010. Omni Press.
2. Gideon Dror, Noam Koenigstein, Yehuda Koren, and Markus Weimer. The yahoo music dataset and kddcup' 11. In Proceedings of KDDCup 2011, 2011.
3. Wei Fan, Joe McCloskey, and Philip S. Yu. A general framework for accurate and fast regression by data summarization in random decision trees. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '06, pages 136-146, New York, NY, USA, 2006. ACM.
4. Wei Fan, Haixun Wang, Philip S. Yu, and Sheng Ma. Is random model better? on its accuracy and efficiency. In Proceedings of the 3rd IEEE International Conference on Data Mining, ICDM '03, pages 51-58, Washington, DC, USA, 2003. IEEE Computer Society.
5. Yoav Freund and Robert E. Schapire. A decisiontheoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55:119-139, 1997.
6. Jerome H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29:1189-1232, 2000.
7. Nicolas Gillis and François Glineur. A multilevel approach for nonnegative matrix factorization. CORE Discussion Papers, abs/1009.0881, 2010.
8. Gene H. Golub and Charles F. Van Loan. Matrix computations (3rd ed.). Johns Hopkins University Press, Baltimore, MD, USA, 1996.
9. Mohsen Jamali and Martin Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pages 135-142, New York, NY, USA, 2010. ACM.
10. Adam Kalai. Efficient estimators for generalized additive models, 2005.
11. Yehuda 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, pages 426- 434, New York, NY, USA, 2008. ACM.
12. Neil D. Lawrence and Raquel Urtasun. Non-linear matrix factorization with gaussian processes. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pages 601-608, New York, NY, USA, 2009. ACM.
13. Bin Li, Qiang Yang, and Xiangyang Xue. Transfer learning for collaborative filtering via a rating-matrix generative model. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pages 617-624, New York, NY, USA, 2009. ACM.
14. Hao Ma, Irwin King, and Michael R. Lyu. Learning to recommend with social trust ensemble. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '09, pages 203-210, New York, NY, USA, 2009. ACM.
15. Lester W. Mackey, David Weiss, and Michael I. Jordan. Mixed membership matrix factorization. In Proceedings of the 27th International Conference on Machine Learning, ICML '10, pages 711-718, Haifa, Israel, 2010. Omni Press.
16. Shie Mannor and Ron Meir. Geometric bounds for generalization in boosting. In Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory, COLT '01, pages 461-472, London, UK, 2001. Springer-Verlag.
17. Aditya Krishna Menon, Krishna-Prasad Chitrapura, Sachin Garg, Deepak Agarwal, and Nagaraj Kota. Response prediction using collaborative filtering with hierarchies and side-information. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pages 141-149, New York, NY, USA, 2011. ACM.
18. A. Murua. Upper bounds for error rates of linear combinations of classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24:591-602, 2002.
19. Michael Peter Perrone. Improving Regression Estimation: Averaging methods for variance reduction with extensions to general convex measure optimization. PhD thesis, 1993.
20. Ruslan Salakhutdinov and Andriy Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In Proceedings of the 25th international conference on Machine learning, ICML '08, pages 880- 887, New York, NY, USA, 2008. ACM.
21. Y. Seldin and N. Tishby. Pac-bayesian analysis of coclustering and beyond. Journal of Machine Learning Research, 11:3595-3646, 2010.
22. Ohad Shamir and Shai Shalev-Shwartz. Collaborative filtering with the trace norm: Learning, bounding, and transducing. In Proceedings of the 24th Annual Conference on Learning Theory, COLT '11, London, UK, 2011. Springer-Verlag.
23. Ajit P. Singh and Geoffrey J. Gordon. Relational learning via collective matrix factorization. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '08, pages 650-658, New York, NY, USA, 2008. ACM.
24. Nathan Srebro, Noga Alon, and Tommi Jaakkola. Generalization error bounds for collaborative prediction with low-rank matrices. In Proceedings of the 17th conference on Advances in Neural Information Processing Systems, NIPS '08, pages 1321-1328, Cambridge, MA, 2004. MIT Press.
25. Nathan Srebro and Adi Shraibman. Rank, trace-norm and max-norm. In Proceedings of the 18th Annual Conference on Learning Theory, COLT '05, pages 545- 560, London, UK, 2005. Springer-Verlag.