Cross-domain recommender systems

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2011, Pages 496-503

  Cremonesi, Paolo ^a   Tripodi, Antonio ^b   Turrin, Roberto ^b  

^a POLITECNICO DI MILANO   (Italy)

^b Moviri Srl   (Italy)

Author keywords

Cross domain; Neighborhood based; Recommender systems; Transitive closure

Indexed keywords

COLLABORATIVE FILTERING; COLLABORATIVE RECOMMENDER SYSTEMS; CROSS-DOMAIN; DATA SETS; DIFFERENT DOMAINS; MOBILE APPLICATIONS; MULTIPLE DOMAINS; NEIGHBORHOOD-BASED; SINGLE DOMAINS; STATE-OF-THE-ART ALGORITHMS; TRANSITIVE CLOSURE; TV PROGRAMS;

ALGORITHMS; RECOMMENDER SYSTEMS;

DATA MINING;

EID: 84857162378     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2011.57     Document Type: Conference Paper

References (18)

1. F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds., Recommender Systems Handbook. Springer, 2011.
2. A. Loizou, "How to recommend music to film buffs: enabling the provision of recommendations from multiple domains," Ph.D. dissertation, 2009, adviser-Srinandan Dasmahapatra and Paul H. Lewis.
3. G. González, B. López, and J. L. de la Rosa, "A multi-agent smart user model for cross-domain recommender systems," in Proceedings of Workshop Beyond Personalization 2005: The Next Stage of Recommender Systems Research. International Conference on Intelligent User Interfaces IUI'05, San Diego, California, USA, 2005.
4. M. M. Tuffield, A. Loizou, and D. Dupplaw, "The semantic logger: supporting service building from personal context," in CARPE '06: Proceedings of the 3rd ACM workshop on Continuous archival and retrieval of personal experiences. New York, NY, USA: ACM, 2006, pp. 55-64.
5. C. H. Lee, Y. H. Kim, and P. K. Rhee, "Web personalization expert with combining collaborative filtering and association rule mining technique," Expert Systems with Applications, vol. 21, no. 3, pp. 131 - 137, 2001.
6. S. Berkovsky, T. Kuflik, and F. Ricci, "Cross-domain mediation in collaborative filtering." in User Modeling, ser. Lecture Notes in Computer Science, C. Conati, K. F. McCoy, and G. Paliouras, Eds., vol. 4511. Springer, 2007, pp. 355-359.
7. F. Zhuang, P. Luo, H. Xiong, Y. Xiong, Q. He, and Z. Shi, "Cross-domain learning from multiple sources: A consensus regularization perspective." IEEE Trans. Knowl. Data Eng., vol. 22, no. 12, pp. 1664-1678, 2010.
8. B. Cao, N. N. Liu, and Q. Yang, "Transfer learning for collective link prediction in multiple heterogenous domains," in ICML, 2010, pp. 159-166.
9. B. Li, Q. Yang, and X. Xue, "Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction." in IJCAI, C. Boutilier, Ed., 2009, pp. 2052-2057.
10. -, "Transfer learning for collaborative filtering via a rating-matrix generative model," in Proceedings of the 26th Annual International Conference on Machine Learning, ser. ICML '09. New York, NY, USA: ACM, 2009, pp. 617-624.
11. Y. Zhang, B. Cao, and D. yan Yeung, "Multi-domain collaborative filtering," in Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, ser. UAI '10, 2010.
12. W. Pan, N. N. Liu, E. W. Xiang, and Q. Yang, "Transfer learning to predict missing ratings via heterogeneous user feedbacks," in Proceedings of the 22nd International Joint Conference on Artificial Intelligence, ser. IJCAI '11, 2011.
13. -, "Transfer learning in collaborative filtering for sparsity reduction," in Proceedings of the 24th AAAI Conference on Artificial Intelligence, ser. AAAI '10, 2010.
14. P. Cremonesi, Y. Koren, and R. Turrin, "Performance of recommender algorithms on top-n recommendation tasks," in RecSys, 2010, pp. 39-46.
15. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, "Item-based collaborative filtering recommendation algorithms," 10th Int. Conf. on World Wide Web, pp. 285-295, 2001.
16. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, Application of Dimensionality Reduction in Recommender System-A Case Study. Defense Technical Information Center, 2000.
17. 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. New York, NY, USA: ACM, 2008, pp. 426-434.
18. F. Wang, S. Ma, L. Yang, and T. Li, "Recommendation on item graphs," in Proceedings of the Sixth International Conference on Data Mining, ser. ICDM '06. Washington, DC, USA: IEEE Computer Society, 2006, pp. 1119-1123.