Recommender systems based on quantitative implicit customer feedback

Decision Support Systems

Volumn 68, Issue , 2014, Pages 77-88

  Bauer, Josef ^a   Nanopoulos, Alexandros ^a  

^a NONE   (Germany)

Author keywords

Data mining; e Commerce; Machine learning; Matrix factorization; Recommender systems

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; DECISION SUPPORT SYSTEMS; ELECTRONIC COMMERCE; FACTORIZATION; LEARNING SYSTEMS; NORMAL DISTRIBUTION; ONLINE SYSTEMS; POISSON DISTRIBUTION; RECOMMENDER SYSTEMS; SALES; VIRTUAL REALITY;

CUSTOMER FEEDBACK; DIFFERENT DISTRIBUTIONS; EXPERIMENTAL EVALUATION; GAMMA DISTRIBUTION; INVERSE GAUSSIAN; MATRIX FACTORIZATIONS; ON-LINE SERVICE; STATE-OF-THE-ART METHODS;

MATRIX ALGEBRA;

EID: 84911950084     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2014.09.005     Document Type: Article

References (23)

1. R. Burke Hybrid web recommender systems The Adaptive Web 2007 Springer 377 408
2. J. Canny Gap: a factor model for discrete data Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in, Information Retrieval 2004 122 129
3. Y. Chen, M. Kapralov, J. Canny, and D. Pavlov Factor modeling for advertisement targeting Advances in Neural Information Processing Systems 2009 324 332
4. C. Cheng, H. Yang, I. King, and M.R. Lyu Fused matrix factorization with geographical and social influence in location-based social networks AAAI'12: Proceedings of the 26th Conference on Artificial Intelligence 2012 17 23
5. Y. Hu, Y. Koren, and C. Volinsky Collaborative filtering for implicit feedback datasets Proceedings of the 8th IEEE International Conference on Data Mining 2008 263 272
6. G. Jawaheer, M. Szomszor, and P. Kostkova Comparison of implicit and explicit feedback from an online music recommendation service Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems 2010 ACM 47 51
7. Y. Kim, and J. Srivastava Impact of social influence in e-commerce decision making Proceedings of the 9th International Conference on Electronic Commerce 2007 ACM 293 302
8. J.A. Konstan, and J. Riedl Deconstructing recommender systems IEEE Spectrum 2012 49 56
9. Y. Koren, R. Bell, and C. Volinsky Matrix factorization techniques for recommender systems Computer 42 2009 30 37
10. K. Lakiotaki, N.F. Matsatsinis, and A. Tsoukias Multicriteria user modeling in recommender systems IEEE Intelligent Systems 26 2011 64 76
11. G. Linden, B. Smith, and J. York Amazon.com recommendations: item-to-item collaborative filtering Internet Computing, IEEE 7 2003 76 80
12. H. Ma, C. Liu, I. King, and M.R. Lyu Probabilistic factor models for web site recommendation Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval 2011 ACM 265 274
13. P. Melville, R.J. Mooney, and R. Nagarajan Content-boosted collaborative filtering for improved recommendations AAAI/IAAI 2002 187 192
14. D.W. Oard, and J. Kim Implicit feedback for recommender systems Proceedings of the AAAI Workshop on Recommender Systems 1998 81 83
15. M.J. Pazzani, and D. Billsus Content-based recommendation systems The Adaptive Web 2007 Springer 325 341
16. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme BPR: Bayesian personalized ranking from implicit feedback Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence 2009 AUAI Press 452 461
17. F. Ricci, L. Rokach, B. Shapira, and P.B. Kantor Recommender Systems Handbook 2011 Springer
18. R. Salakhutdinov, and A. Mnih Probabilistic matrix factorization Advances in Neural Information Processing Systems 20 2008 1257 1264
19. J.B. Schafer, D. Frankowski, J. Herlocker, and S. Sen Collaborative filtering recommender systems The Adaptive Web 2007 Springer 291 324
20. J.B. Schafer, J.A. Konstan, and J. Riedl E-commerce recommendation applications Applications of Data Mining to Electronic Commerce 2001 Springer 115 153
21. A.P. Singh, and G.J. Gordon A unified view of matrix factorization models ECML PKDD 2008 Springer 358 373
22. X. Su, and T.M. Khoshgoftaar A survey of collaborative filtering techniques Advances in Artificial Intelligence 2009 2009 1 19
23. M. Svensson, K. Höök, and R. Cöster Designing and evaluating kalas: a social navigation system for food recipes ACM Transactions on Computer-Human Interaction (TOCHI) 12 2005 374 400