Learning attribute-to-feature mappings for cold-start recommendations

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2010, Pages 176-185

  Gantner, Zeno ^a   Drumond, Lucas ^a   Freudenthaler, Christoph ^a   Rendle, Steffen ^a   Schmidt Thieme, Lars ^a  

^a UNIVERSITY OF HILDESHEIM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COLD-START; CONSTANT FACTORS; FACTORIZATION MODEL; FEATURE MAPPING; GEOGRAPHICAL LOCATIONS; HIGHER-DIMENSIONAL; MATRIX; MATRIX FACTORIZATIONS; PREDICTIVE ACCURACY; PRODUCT CATEGORIES;

DATA MINING; FACTORIZATION; MAPPING; RECOMMENDER SYSTEMS;

MATRIX ALGEBRA;

EID: 79951727814     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2010.129     Document Type: Conference Paper

References (27)

1. D. Agarwal and B.-C. Chen. Regression-based latent factor models. In J. F. E. IV, F. Fogelman-Souli, P. A. Flach, and M. Zaki, editors, KDD, pages 19-28. ACM, 2009.
2. D. Agarwal and B.-C. Chen. fLDA: Matrix factorization through latent dirichlet allocation. In Davison et al. [8], pages 91-100.
3. K. Ali and W. van Stam. Tivo: Making show recommendations using a distributed collaborative filtering architecture. In Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2004.
4. L. D. Bergman, A. Tuzhilin, R. D. Burke, A. Felfernig, and L. Schmidt-Thieme, editors. Proceedings of the 2009 ACM Conference on Recommender Systems, RecSys 2009, New York, NY, USA, October 23-25, 2009. ACM, 2009.
5. D. Billsus, M. J. Pazzani, and J. Chen. A learning agent for wireless news access. In Intelligent User Interfaces, pages 33-36, 2000.
6. D. Blei, A. Ng, and M. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
7. P. Cremonesi and R. Turrin. Analysis of cold-start recommendations in iptv systems. In Bergman et al. [4], pages 233-236.
8. B. D. Davison, T. Suel, N. Craswell, and B. Liu, editors. Proceedings of the Third International Conference on Web Search and Web Data Mining, WSDM 2010. ACM, 2010.
9. A. Gunawardana and C. Meek. Tied boltzmann machines for cold start recommendations. In RecSys '08: Proceedings of the 2008 ACM Conference on Recommender Systems, pages 19-26, New York, NY, USA, 2008. ACM.
10. A. Gunawardana and C. Meek. A unified approach to building hybrid recommender systems. In Bergman et al. [4], pages 117-124.
11. R. Harshman. Foundations of the parafac procedure: models and conditions for an 'exploratory' multimodal factor analysis. In UCLA Working Papers in Phonetics, pages 1-84, 1970.
12. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning, Second Edition: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer, 2nd ed. corr. 3rd printing edition, January 2009.
13. T. Hofmann and J. Puzicha. Latent class models for collaborative filtering. In Proc. of the Sixteenth International Joint Conference on Artificial Intelligence, pages 688-693, Stockholm, 1999.
14. Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30-37, 2009.
15. C. D. Manning, P. Raghavan, and H. Schütze. Introduction to Information Retrieval. Cambridge University Press, Cambridge, UK, 2008.
16. D. Oard and J. Kim. Implicit feedback for recommender systems. In in Proceedings of the AAAI Workshop on Recommender Systems, pages 81-83, 1998.
17. R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. M. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. In ICDM 2008, 2008.
18. S.-T. Park and W. Chu. Pairwise preference regression for cold-start recommendation. In Bergman et al. [4], pages 21-28.
19. A. Paterek. Improving regularized singular value decomposition for collaborative filtering. In KDD Cup Workshop at KDD-07, San Jose, California, USA, 2007.
20. M. Pazzani and D. Billsus. Content-based recommendation systems. In The Adaptive Web, pages 325-341. 2007. (Pubitemid 351189202)
21. I. Pilaszy and D. Tikk. Recommending new movies: even a few ratings are more valuable than metadata. In Bergman et al. [4], pages 93-100.
22. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In UAI 2009, 2009.
23. S. Rendle and L. Schmidt-Thieme. Pairwise interaction tensor factorization for personalized tag recommendation. In Davison et al. [8], pages 81-90.
24. B. M. Sarwar, J. A. Konstan, A. Borchers, J. Herlocker, B. Miller, and J. Riedl. Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In Computer Supported Cooperative Work, pages 345-354, 1998.
25. A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock. Methods and metrics for cold-start recommendations. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and development in information retrieval, pages 253-260, New York, NY, USA, 2002. ACM Press.
26. A. J. Smola and B. Schölkopf. A tutorial on support vector regression. Statistics and Computing, 14(3):199-222, 2004. (Pubitemid 39063488)
27. L. Tucker. Some mathematical notes on three-mode factor analysis. Psychometrika, pages 279-311, 1966.