The rapid development of e-commerce has promoted the growth of online auctions business based on C2C context. However, the ever-increasing customer size and auctioned goods cause the problem of information overload, and how to enhance the customer loyalty becomes a critical issue faced by most online auctions websites. One way to overcome the problem is to use recommender systems to provide personalized information services. Since there exist much difference between B2C and C2C context, it is a new challenge for us to apply recommender systems to the latter setting. This paper analyzes the customer behaviors on the auction website and constructs the customer preference model under the C2C context. Then the collaborative filtering technique is used to recommend auction items.
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