Fast context-aware recommendations with factorization machines

SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2011, Pages 635-644

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

^a UNIVERSITY OF KONSTANZ   (Germany)

^b UNIVERSITY OF HILDESHEIM   (Germany)

Author keywords

Context aware recommender system; Factorization machine; Rating prediction; Tensor factorization

Indexed keywords

FORECASTING; ITERATIVE METHODS; LEAST SQUARES APPROXIMATIONS; RECOMMENDER SYSTEMS; TENSORS;

CONTEXT-AWARE RECOMMENDATIONS; CONTEXT-AWARE RECOMMENDER SYSTEMS; CONTEXTUAL INFORMATION; FACTORIZATION MACHINES; ITERATIVE OPTIMIZATION; LEAST-SQUARE SOLUTION; PREDICTIVE ACCURACY; TENSOR FACTORIZATION;

FACTORIZATION;

EID: 80052108915     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2009916.2010002     Document Type: Conference Paper

References (21)

1. G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems, 23(1):103-145, 2005. (Pubitemid 40245827)
2. D. Agarwal and B.-C. Chen. Regression-based latent factor models. In KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 19-28, New York, NY, USA, 2009. ACM.
3. L. Baltrunas and F. Ricci. Context-based splitting of item ratings in collaborative filtering. In RecSys '09: Proceedings of the third ACM conference on Recommender systems, pages 245-248, New York, NY, USA, 2009. ACM.
4. R. M. Bell and Y. Koren. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), October 28-31, 2007, Omaha, Nebraska, USA, pages 43-52. IEEE Computer Society, 2007.
5. A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In RecSys '10: Proceedings of the fourth ACM conference on Recommender systems, pages 79-86, New York, NY, USA, 2010. ACM.
6. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 426-434, New York, NY, USA, 2008. ACM.
7. Y. Koren. Collaborative filtering with temporal dynamics. In KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 447-456, New York, NY, USA, 2009. ACM.
8. Y. Li, J. Nie, Y. Zhang, B. Wang, B. Yan, and F. Weng. Contextual recommendation based on text mining. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING '10, pages 692-700, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics.
9. K. Oku, S. Nakajima, J. Miyazaki, and S. Uemura. Context-aware svm for context-dependent information recommendation. In MDM '06: Proceedings of the 7th International Conference on Mobile Data Management, page 109, Washington, DC, USA, 2006. IEEE Computer Society.
10. C. Ono, Y. Takishima, Y. Motomura, and H. Asoh. Context-aware preference model based on a study of difference between real and supposed situation data. In UMAP '09: Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization, pages 102-113, Berlin, Heidelberg, 2009. Springer-Verlag.
11. U. Panniello, A. Tuzhilin, M. Gorgoglione, C. Palmisano, and A. Pedone. Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. In RecSys '09: Proceedings of the third ACM conference on Recommender systems, pages 265-268, New York, NY, USA, 2009. ACM.
12. I. Pilászy and D. Tikk. Recommending new movies: even a few ratings are more valuable than metadata. In RecSys '09: Proceedings of the third ACM conference on Recommender systems, pages 93-100, New York, NY, USA, 2009. ACM.
13. I. Pilászy, D. Zibriczky, and D. Tikk. Fast als-based matrix factorization for explicit and implicit feedback datasets. In RecSys '10: Proceedings of the fourth ACM conference on Recommender systems, pages 71-78, New York, NY, USA, 2010. ACM.
14. S. Rendle. Factorization machines. In Proceedings of the 10th IEEE International Conference on Data Mining. IEEE Computer Society, 2010.
15. S. Rendle and L. Schmidt-Thieme. Pairwise interaction tensor factorization for personalized tag recommendation. In WSDM '10: Proceedings of the third ACM international conference on Web search and data mining, pages 81-90, New York, NY, USA, 2010. ACM.
16. A. Said, S. Berkovsky, and E. W. De Luca. Putting things in context: Challenge on context-aware movie recommendation. In CAMRa2010: Proceedings of the RecSys '10 Challenge on Context-aware Movie Recommendation, New York, NY, USA, 2010. ACM.
17. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285-295, New York, NY, USA, 2001. ACM Press.
18. N. Srebro, J. D. M. Rennie, and T. S. Jaakola. Maximum-margin matrix factorization. In Advances in Neural Information Processing Systems 17, pages 1329-1336. MIT Press, 2005.
19. D. H. Stern, R. Herbrich, and T. Graepel. Matchbox: large scale online bayesian recommendations. In Proceedings of the 18th international conference on World wide web, WWW '09, pages 111-120, New York, NY, USA, 2009. ACM.
20. L. Tucker. Some mathematical notes on three-mode factor analysis. Psychometrika, 31:279-311, 1966.
21. L. Xiong, X. Chen, T.-K. Huang, J. Schneider, and J. G. Carbonell. Temporal collaborative filtering with bayesian probabilistic tensor factorization. In Proceedings of the SIAM International Conference on Data Mining (SDM 2010), pages 211-222. SIAM, 2010.