Mining customer product ratings for personalized marketing

Kwok Wai CHEUNG*, James T. Kwok, Martin H. Law, Kwok Ching Tsui

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

214 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)231-243
Number of pages13
JournalDecision Support Systems
Issue number2
Publication statusPublished - May 2003

Scopus Subject Areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

User-Defined Keywords

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


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