An online Bayesian networks model for E-commercial personalized recommendation system

Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007

Volumn 6, Issue , 2007, Pages 3506-3511

  Zhang, Shao Zhong ^a,b   Liu, Lu ^a   Dong, Yu Zhi ^c  

^a BEIHANG UNIVERSITY   (China)

^b ZHEJIANG WANLI UNIVERSITY   (China)

^c Wuxi HongGuang Microelectronics Ltd   (China)

Author keywords

Bayesian networks; E commercial recommended systems; Online model

Indexed keywords

ELECTRONIC COMMERCE; FUNCTIONAL ANALYSIS; LEARNING ALGORITHMS; MATHEMATICAL MODELS; ONLINE SYSTEMS; PARAMETER ESTIMATION;

E-COMMERCIAL RECOMMENDED SYSTEMS; ONLINE ALGORITHM; ONLINE MODEL;

BAYESIAN NETWORKS;

EID: 38049003086     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLC.2007.4370754     Document Type: Conference Paper

References (15)

1. Sarwar B.M., Karypis G., Konstan J.A., et al. Analysis of recommendation algorithms for e-commerce. Proceedings of the ACM EC'00 Conference [C]. Minneapolis, MN., 2000. 158-167.
2. Schafer J.B., Konstan J.A., Riedl J.. Recommender systems in e-commerce. Proceedings of the First ACM Conference on Electronic Commerce [C]. Denver, CO, 1999. 158-166.
3. Ben J., Konstan J.A., John R.. E-commerce recommendation applications [R]. University of Minnesota, 2001
4. Schafer J.B., Konstan J.A., Riedl J.. E-Commerce recommendation applications [J]. Data Mining and Knowledge Discovery, 2001, 5 (1-2): 115-153.
5. Heckeman D. Bayesian networks for data mining. [J]. Data Mining and Knowledge Discovery. 1997.1:79-119.
6. Chickering D and Hecherman D. Efficient approximations for the marginal likelihood of bayesian networks with hidden variables [J]. Machine Learning, 1997, 29, 181-212.
7. Zan Huang, Wingyan Chung, Hsinchun Chen, A graph model for E-commerce recommender systems [J], Journal of the American Society for Information Science and Technology, 2004,55(3):259-274.
8. Ji Jun-zhong, A Method of Commodity Recommendation Based on Customer Shopping Model of Bayesian Network [J], Application research of computers, 2005,22(4): 65-71.
9. Thiesson B, Meek C, Chickering D and Heckerman D. Learning mixture of DAG models[R]. Proceedings of the Fourteenth Conference on Uncertainty in Articial Intelligence, 1998,504-513.
10. Dempster A, Laird N and Rubin D. Maximum likelihood from incomplete data via the EM algorithm [J]. Journal of the Royal Statistical Society, 1977, 38(1): 1-38.
11. S.L. Lauritzen. The EM algorithm for graphical association models with missing data. [J]. Comput. Stat. Data Anal. 1995.19:191-201
12. Ira Cohen, Alexandre Bronstein, Fabio G.Cozman. Online learning of Bayesian network parameters. http://www.hpl.hp.com/techreports/2001/HPL-2001- 156.pdf.
13. Eric Bauer, Daphne Koller, Yoram Singer. Update rules for parameter estimation in Bayesian networks. [P] In Proceeding of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), Providence, Rhode Island, August, 1997,1-3:3-13.
14. Cooper G. Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Machine Learning. 1992.9:309-347.
15. MovieLens, http://movielens.umn.edu/.