Mining customer preference ratings for product recommendation using the support vector machine and the latent class model

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

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

As Internet commerce becomes more popular, customers' preferences on various products can now be readily acquired on-line via various e-commerce systems. Properly mining this extracted data can generate useful knowledge for providing personalized product recommendation services. In general, recommender systems use two complementary techniques. Content-based systems match customer interests with products attributes, while collaborative filtering systems utilize preference ratings from other customers. In this paper, we address some problems faced by these two systems, and study how machine learning techniques, namely the support vector machine and the latent class model, can be used to alleviated them.

Original languageEnglish
Pages (from-to)601-610
Number of pages10
JournalManagement Information Systems
Volume2
Publication statusPublished - 2000
EventSecond International Conference on Data Mining, Data Minig II - Cambridge, United Kingdom
Duration: 5 Jul 20007 Jul 2000

Scopus Subject Areas

  • Management Information Systems
  • Information Systems
  • Engineering(all)
  • Computer Science Applications
  • Information Systems and Management

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