WIT Press


Mining Customer Preference Ratings For Product Recommendation Using The Support Vector Machine And The Latent Class Model

Price

Free (open access)

Paper DOI

10.2495/DATA000581

Volume

25

Pages

10

Published

2000

Size

1,014 kb

Author(s)

William K. Cheung, James T. Kwok, Martin H. Law & Kwok-Ching Tsui

Abstract

As Internet commerce becomes more popular, customers' preferences on var- ious 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 collabo- rative filtering systems utilize preference ratings from other customers. In this paper, we address some problems faced by these two syste

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