Research on entropy-based collaborative filtering algorithm and personalized recommendation in e-commerce

Service Oriented Computing and Applications

Volumn 3, Issue 2, 2009, Pages 147-157

  Piao, Chun Hui ^a,b   Zhao, Jing ^b   Zheng, Li Juan ^b  

^a RENMIN UNIVERSITY OF CHINA   (China)

^b SHIJIAZHUANG TIEDAO UNIVERSITY   (China)

Author keywords

B2B e commerce; Collaborative filtering algorithm; Entropy; MAE; Personalized recommendation; Similarity measure; Web services

Indexed keywords

B2B E-COMMERCE; COLLABORATIVE FILTERING ALGORITHM; MAE; PERSONALIZED RECOMMENDATION; SIMILARITY MEASURE;

ELECTRONIC COMMERCE; ENTROPY; FILTRATION; SIGNAL FILTERING AND PREDICTION; WEB SERVICES;

ALGORITHMS;

EID: 67651100688     PISSN: 18632386     EISSN: 18632394     Source Type: Journal    
DOI: 10.1007/s11761-008-0034-3     Document Type: Article

References (28)

1. Yu L, Liu L (2004) Research on personalized recommendations in e-business. Comput Integrated Manuf Syst 10(10)
2. Zeng Y, Mai Y-H (2004) Collaborative filtering recommendation based on content and item rating prediction. Comput Appl 24(1)
3. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative Filtering recommendation algorithms. In: Proceedings of the Tenth International World Wide Web Conference, pp 285-295
4. D Chickering D Hecherman 1997 Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables Mach Learn 29 181 212 10.1023/A:1007469629108 (Pubitemid 127510038)
5. Dempster A, Laird N, Rubin D (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc
6. Thiesson B, Meek C, Chickering D, Heckerman D (1997) Learning mixture of DAG Models. Technical Report MSR-TR-97-30, Microsoft Research, Redmond, WA
7. Greg L, Brent S, Jeremy Y (2003) Amazon.com Recommendations Item-to-Item Collaborative Filtering
8. Sarwar BM, Karypis G, Konstan JA, Riedl J (2000) Application of dimensionality reduction in recommender system-a case study. In: ACM WebKDD 2000 Workshop
9. Aggarwal CC (2001) On the effects of dimensionality reduction on high dimensional similarity search. ACM PODS Conference
10. Zheng A-L, Zhu Y-Y, Shi B-L (2002) Collaborative filtering recommendation algorithm based on item rating prediction. J Softw 13(4)
11. Sarwar B, Karypis G, Konstan J, Riedl J (2000) Analysis of recommendation algorithms for e-commerce. ACM Conference on Electronic Commerce, pp 158-167
12. Breese J, Hecherman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98), pp 43-52
13. Zhao Z, Shi B (2005) An adaptive algorithm for personal recommendation. J Changchun Univ 15(6)
14. Wang J, de Vries AP, Reinders MJT (2006) A user-item relevance model for log-based collaborative filtering
15. Pazzani MJ (1999) A framework for collaborative, content-based and demographic filtering
16. Zhao L, Hu N-J, Zhang S-Z (2002) Algorithm design for personalization recommendation systems. J Comput Res Dev 39(8)
17. Wang X, Liu Q (2005) Research on application of collaborative filtering in recommender systems. Comput Syst Appl (4)
18. P Ganesan H Garcia-Molina J Widom 2003 Exploiting hierarchical domain structure to compute similarity ACM Trans Inf Syst 21 1 64 93 10.1145/635484.635487
19. Rashid AM, Albert I, Cosley D, Lam SK, McNee SM, Konstan JA, Riedl J (2002) Getting to know you: learning new user preferences in recommender systems. In: Proceeding of the international conference on intelligent user interfaces (IUI), pp 127-134
20. Kohrs A, Merialdo B (2001) Improving collaborative filtering for new users by smart object selection. In: Proceedings of International Conference on Media Features (ICMF) (oral presentation)
21. Jin X, Zhou Y, Mobasher B (2005) A maximum entropy web recommendation system: combining collaborative and content features. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 621-617
22. Pavlov D, Pennock D (2002) A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains. In: Proceedings of Neural Information Processing Systems 2002
23. Jin X, Zhou Y, Mobasher B (2005) Task-oriented web user modeling for recommendation. In: Proceedings of the 10th International Conference on User Modeling (UM'05), UK, April
24. Zitnick C, Kanade T (2004) Maximum entropy for collaborative filtering. In: Proceedings of 20th International Conference on Uncertainty in Artificial Intelligence (UAI'04), Banff, Canada, July
25. Wang W-Q, Xin X-L (2005) Hybrid partial entropy and relative entropy. Fuzzy Syst Math 19(4)
26. Shannon CE (1948) A mathematical theory of communication. Bell System Tech 27:379-423, 623-656
27. http://www.informatik.uni-freiburg.de/~cziegler/BX/. 750 2007-11-15
28. Curbera F, Nagy WA, Weerawarana S (2001) Web Services: Why and How. IBM T.J. Watson Research Center