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2003 IEEE/WIC International Conference on Web Intelligence (WI'03)   p. 68
Online Recommendation Based on Customer Shopping Model in E-Commerce
Junzhong Ji, Beijing University of Technology
Zhiqiang Sha, Beijing University of Technology
Chunnian Liu, Beijing University of Technology
Ning Zhong, Maebashi Institute of Technology

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WI.2003.1241175
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Abstract
As e-commerce developing rapidly, it is becoming a research focus about how to capture or find customer’s behavior patterns and realize commerce intelligence by use of Web mining technology. Recommendation system in electronic commerce is one of the successful applications that are based on such mechanism. In this paper, we present a new framework in recommendation system by finding customer model from business data. This framework formalizes the recommending process as knowledge representation of the customer shopping information and uncertainty knowledge inference process. In our approach, we firstly build a customer model based on Bayesian network by learning from customer shopping history data, then we present a recommendation algorithm based on probability inference in combination with the last shopping action of the customer, which can effectively and in real time generate a recommendation set of commodity.
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Citation:  Junzhong Ji, Zhiqiang Sha, Chunnian Liu, Ning Zhong, "Online Recommendation Based on Customer Shopping Model in E-Commerce," wi, p. 68,  2003 IEEE/WIC International Conference on Web Intelligence (WI'03),  2003