| Abstract: | The prosperity of electronic commerce has changed the traditional trading behaviors and more and more people are willing to conduct Internet shopping. However, the exponentially increasing information provided by the Internet enterprises causes the problem of overloaded information, and this inevitably reduces the customer's satisfaction and loyalty. One way to overcome such a problem is to build personalized recommender systems to retrieve product information that really interests the customers. For products that people may purchase relatively often, such as books and CDs, recommender systems can be built to reason about a customer's personal preferences from his purchasing history and then provide the most appropriate information services to meet his needs. On the other hand, for those commodities a general customer does not buy frequently, for example computers and home theater systems, more appropriate are the kinds of recommender systems able to retrieve optimal products based on the customer's current preferences obtained from the iterative system–customer interactions. This paper presents the above two kinds of recommender systems we have developed for supporting Internet commerce. Experimental results show the promise of our systems. |
