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Collaborative filtering algorithm is one of the most successful technologies used in personalized recommendation system. However, traditional algorithms focus only on user ratings and do not consider the changes of user interest and the credibility of ratings data, which affected the quality of the system's recommendation seriously. To solve this problem, this paper presents an improved algorithm. Firstly, the user's rating is given a weight by a gradual time decrease and credit assessment in the course of user similarity measurement, and then several users highly similar with active user are selected as his neighbor. Finally, the active user's preference for an item can be represented by the average scores of his neighbor.
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