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| Author: | Cheung, K.-W. |
| Title: | Mining Customer Product Ratings for Personalized Marketing |
| Journal: | Decision Support Systems
2003 : MAY, VOL. 35:2, p. 231-244 |
| Index terms: | MARKETING MODELS CUSTOMERS MARKETING MODELS |
| Language: | eng |
| 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, recommender systems are based on two complementary techniques. In this paper, the authors 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. |
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