[Objective] Solve the problems in the traditional collaborative filtering recommendation algorithm, such as sparse data and user's interests in different time being considered equally.[Methods] This paper proposes a collaborative filtering algorithm based on user's interest fuzzy clustering. In the algorithm, the model of user's interest consists of the stable interest and the current interest. Users are clustered by the fuzzy clustering according to the stable interest, then the nearest neighbours and the initial recommendation list can be obtained. The final recommendation list is generated by sorting the similarity between the each item of initial recommendation list and user current interest, on the basis of the initial recommendations. [Results] The Mean Absolute Error (MAE) of the proposed method is nearly 10% reduction verified on the MovieLens dataset, compared with the traditional method.[Limitations] All categories of projects are considered in the model of the user stable interest without special treatments, such as merge and delete.[Conclusions] The experiment result indicates that the recommendation accuracy of the advanced approach is more efficiency, compared with the traditional recommendation algorithm.
刘占兵, 肖诗斌. 基于用户兴趣模糊聚类的协同过滤算法[J]. 现代图书情报技术, 2015, 31(11): 12-17.
Liu Zhanbing, Xiao Shibin. Collaborative Filtering Recommended Algorithm Based on User's Interest Fuzzy Clustering. New Technology of Library and Information Service, 2015, 31(11): 12-17.
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