Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Mining customer product ratings for personalized marketing

Language

English

Abstract

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.

Keywords

Recommender systems, Personalized marketing, Support vector machine, Latent class model

Publication Date

5-2003

Source Publication Title

Decision Support Systems

Volume

35

Issue

2

Start Page

231

End Page

243

Publisher

Elsevier

Peer Reviewed

1

Funder

This research has been partially supported by the Research Grants Council of the Hong Kong Special Administrative Region under grant HKUST2033/00E and the Hong Kong Baptist University under grant FRG/99-00/II-36P.

DOI

10.1016/S0167-9236(02)00108-2

Link to Publisher's Edition

http://dx.doi.org/10.1016/S0167-9236(02)00108-2

ISSN (print)

01679236

ISSN (electronic)

18735797

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