[Objective] Utilizing tags frequency and time used by the user, discussing the impact of dynamic changes of user interest for personalized recmmendation accuracy. [Methods] Constructing model for personalized recom-mendation based on social tagging in P2P environment, illustrating the calculation of user preferences and recommended process in detail. Making an experiment to verify the validity of the model using P2P movie sharing system. [Results] In 10 randomly selected target users, the hit rate of recommendation for eight users is higher than traditonal collabrative filtering which is based on scores, proving the advantages of making full use of tag frequency and time factor to recommend. [Limitations] Due to the main task of this paper is to reseach the impact of dynamic changes of user interst for personalized recommendation, so only delete meaningless tags and merge similar tags by hands, do not have an effective mechanism to control the ambiguity of tags. [Conclusions] Considering the dynamic changes of user interest can help to improve the accuracy of personalized recommendation.
赵艳, 王亚民. P2P环境下基于社会化标签的个性化推荐模型研究[J]. 现代图书情报技术, 2014, 30(5): 50-57.
Zhao Yan, Wang Yamin. Model for Personalized Recommendation Based on Social Tagging in P2P Environment. New Technology of Library and Information Service, 2014, 30(5): 50-57.
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