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There are some shortcomings in user-based collaborative filtering, this paper proposed a new collaborative filtering recommendation algorithms, named user's gravitation based collaborative filtering (UGBCF) recommendation algorithm, it used a new method of similarity measure to improve the user-based collaborative recommendation algorithms. This paper thought that the social tags used by user can reflect user’s preference and how much the preference, so it used those social tags to build user's preference object model. It computed the gravitation between preference objects, viewed the gravitation as the similarity of users. According to the similarity, the neighbor user of the target user could be gotten, and the prediction score of his unselected items could be calculated by aggregated the neighbor users' score. The results of experiment show that UGBCF can provide better recommendation quality than other collaborative filtering recommendation. [ABSTRACT FROM AUTHOR]