Personalized recommendation via Cross-Domain Triadic Factorization

WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web

Volumn , Issue , 2013, Pages 595-605

  Hu, Liang ^a   Cao, Jian ^a   Xu, Guandong ^b   Cao, Longbing ^b   Gu, Zhiping ^c   Zhu, Can ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c SHANGHAI TECHNICAL INSTITUTE OF ELECTRONICS AND INFORMATION   (China)

Author keywords

Cross Domain Collaborative Filtering; Recommender system; Triadic factorization

Indexed keywords

ECONOMIC AND SOCIAL EFFECTS; FACTORIZATION; GENETIC ALGORITHMS; RECOMMENDER SYSTEMS; WORLD WIDE WEB;

CROSS-DOMAIN; DATA SPARSITY PROBLEMS; EXPLICIT FEEDBACK; PERSONALIZED RECOMMENDATION; REAL-WORLD DATASETS; REAL-WORLD SCENARIO; TRIADIC RELATIONS; WEIGHT PARAMETERS;

COLLABORATIVE FILTERING;

EID: 84891773848     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2488388.2488441     Document Type: Conference Paper

References (27)

1. Berkovsky, S., Kuflik, T., and Ricci, F., 2007. Cross-Domain Mediation in Collaborative Filtering. In Proceedings of the 11th international conference on User Modeling Springer-Verlag, Corfu, Greece, 355-359.
2. Bro, R., 1998. Multi-way analysis in the food industry: models, algorithms, and applications University of Amsterdam
3. Goldberg, D., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional.
4. Harshman, R.A., 1972. PARAFAC2: Mathematical and technical notes. UCLA Working Papers in Phonetics 22, 30-44.
5. Hofmann, T., 2004. Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22, 1, 89-115.
6. Hu, Y., Koren, Y., and Volinsky, C., 2008. Collaborative filtering for implicit feedback datasets IEEE, 263-272.
7. Kiers, H.A.L., ten Berge, J.M.F., and Bro, R., 1999. PARAFAC2-Part I. A direct fitting algorithm for the PARAFAC2 model. Journal of Chemometrics 13, 3-4, 275-294.
8. Kolda, T.G. and Bader, B.W., 2009. Tensor decompositions and applications. SIAM review 51, 3, 455-500.
9. Koren, Y., Bell, R., and Volinsky, C., 2009. Matrix factorization techniques for recommender systems. Computer 42, 8, 30-37.
10. Leskovec, J., Adamic, L.A., and Huberman, B.A., 2007. The dynamics of viral marketing. ACM Trans. Web 1, 1, 5.
11. Li, B., 2011. Cross-Domain Collaborative Filtering: A Brief Survey. In Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence IEEE Computer Society, 1085-1086.
12. Li, B., Yang, Q., and Xue, X., 2009. Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction. In IJCAI Morgan Kaufmann Publishers Inc., 2052-2057.
13. Li, B., Yang, Q., and Xue, X., 2009. Transfer learning for collaborative filtering via a rating-matrix generative model. In Proceedings of the 26th Annual International Conference on Machine Learning ACM, Montreal, Quebec, Canada, 617-624.
14. Long, M., Wang, J., Ding, G., Cheng, W., Zhang, X., and Wang, W., 2012. Dual transfer learning. In Proceedings of the 12th SIAM International Conference on Data Mining
15. Mørup, M., 2011. Applications of tensor (multiway array) factorizations and decompositions in data mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1, 1, 24-40.
16. Ma, H., Yang, H., Lyu, M.R., and King, I., 2008. SoRec: social recommendation using probabilistic matrix factorization. In Proceeding of the 17th ACM conference on Information and knowledge management ACM, Napa Valley, California, USA, 931-940.
17. Marlin, B.M., Zemel, R.S., Roweis, S., and Slaney, M., 2007. Collaborative filtering and the missing at random assumption. In Proceeding 23rd Conference on Uncertainty in Artificial Intelligence
18. Pan, W., Xiang, E.W., Liu, N.N., and Yang, Q., 2010. Transfer learning in collaborative filtering for sparsity reduction. In AAAI
19. Pan, W., Xiang, E.W., Liu, N.N., and Yang, Q., 2010. Transfer learning in collaborative filtering for sparsity reduction. In Proceedings of the 24th AAAI Conference on Artificial Intelligence
20. Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L., 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence AUAI Press, Montreal, Quebec, Canada, 452-461.
21. Rendle, S., Freudenthaler, C., and Schmidt-Thieme, L., 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web ACM, Raleigh, North Carolina, USA, 811-820.
22. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J., 1994. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work ACM, Chapel Hill, North Carolina, United States, 175-186.
23. Schein, A.I., Popescul, A., Ungar, L.H., and Pennock, D.M., 2002. Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval ACM, Tampere, Finland, 253-260.
24. Singh, A.P. and Gordon, G.J., 2008. Relational learning via collective matrix factorization. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining ACM, Las Vegas, Nevada, USA, 650-658.
25. Srebro, N. and Jaakkola, T., 2003. Weighted low-rank approximations. In Proceedings of the Twentieth International Conference on Machine Learning, Washington DC, 720.
26. Su, X. and Khoshgoftaar, T.M., 2009. A survey of collaborative filtering techniques. Adv. in Artif. Intell. 2009, 2-2.
27. Tomasi, G. and Bro, R., 2005. PARAFAC and missing values. Chemometrics and Intelligent Laboratory Systems 75, 2, 163-180.