Department of Computer Science
Mining customer product ratings for personalized marketing
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.
Recommender systems, Personalized marketing, Support vector machine, Latent class model
Source Publication Title
Decision Support Systems
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.
Link to Publisher's Edition
Cheung, K., Kwok, J., Law, M., & Tsui, K. (2003). Mining customer product ratings for personalized marketing. Decision Support Systems, 35 (2), 231-243. https://doi.org/10.1016/S0167-9236(02)00108-2