Mining customer product rating for personalized marketing

被引:166
作者
Cheung, KW [1 ]
Kwok, JT
Law, MH
Tsui, KC
机构
[1] Hong Kong Baptist Univ, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China
[2] Hong Kong Univ Sci & Technol, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China
关键词
recommender systems; personalized marketing; support vector machine; latent class model;
D O I
10.1016/S0167-9236(02)00108-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the increasing popularity of Internet commerce, a wealth of information about the customers can now be readily acquired on-line. An important example is the customers' preference ratings for the various products offered by the company. Successful mining of these ratings can thus allow the company's direct marketing campaigns to provide automatic product recommendations. In general, these recommender systems are based on two complementary techniques. Content-based systems match customer interests with information about the products, while collaborative systems utilize preference ratings from the other customers. In this paper, we address some issues faced by these systems, and study how recent machine learning algorithms, namely the support vector machine and the latent class model, can be used to alleviate these problems. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:231 / 243
页数:13
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