Abstract:

The rapid development of internet technology, especially the developed of Web 2.0 which has the main feature of personality, making a lot of information in front of people at the same time. The value of personalized recommendation technology has become increasingly prominent. A design of personalized recommender system adapting to different business was presented, including the collection and analysis of user's all explicit and implicit behavior, using user behavior profiles and entropy method to determine the weight of the behavior for analyzing user interest, the introduction of time forgetting function to solve the problem of user interest drift, and getting a list of the user's personalized recommendation based on collaborative filtering technology. Finally, the system test assessment analysis based on the actual data was provided.

Key words: personalized recommender system, user interest analysis, entropy, interest drift, collaborative filtering