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In recent years, the B2C e-commerce achieved a rapid development on a global scope; more and more people began to use the Internet for shopping. However, the exponentially increasing information provided by Internet enterprises causes the problem of overloaded information, and this inevitably reduces the customer's satisfaction and loyalty. One way to overcome such problem is to build personalized recommender systems to retrieve product information that really interests the customers. The rapid development of Web 2.0 provides new ideas for personalized recommendation. In this paper we introduce the collaborative filtering, knowledge-based approaches and hybrid approaches in building recommender systems and discuss the strengths and weaknesses of each approach, we propose a collaborative tagging system to provide personalized product information to customers in B2C e-commerce websites and describe the system's architecture and point the system's advantage.