A Personalized Recommender Systems Framework Based on Ontology and Bayesian Network in E-Commerce

Abstract:

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Personalized recommendation methods are mainly classified into content-based recommendation approach and collaborative filtering recommendation approach. However, Both recommendation approaches have their own drawbacks such as sparsity, cold-start and scalability. To overcome the drawbacks, In this paper, we propose a framework for recommender systems that join use of Ontology and Bayesian Network. On the one hand, Ontology help formally defining the semantics of variables included in the Bayesian network, thus allowing logical reasoning on them. On the other hand, Bayesian network allow reasoning under uncertainty, that is not possible only with the use of ontology. In the recommendation, products not yet purchased or rarely purchased can still be recommended to customers with accuracy.

Info:

Periodical:

Advanced Materials Research (Volumes 143-144)

Edited by:

H. Wang, B.J. Zhang, X.Z. Liu, D.Z. Luo, S.B. Zhong

Pages:

961-965

DOI:

10.4028/www.scientific.net/AMR.143-144.961

Citation:

F. Long et al., "A Personalized Recommender Systems Framework Based on Ontology and Bayesian Network in E-Commerce", Advanced Materials Research, Vols. 143-144, pp. 961-965, 2011

Online since:

October 2010

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Price:

$35.00

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