Abstract：To improve the low accuracy caused by sparse data in the recommender system, a collaborative filtering recommendation algorithm based on energy diffusion in social networks is proposed. The abundant social information in social network and the excellent performance of energy diffusion in data sparsity are combined. Firstly, the transitivity of user-item scoring matrix and trust relationship is exploited to calculate the trust intensity value between users. Then, the resource value of items is obtained by combining the social network with the user-item binary network. Finally, the collaborative filtering method is utilized to predict the score. Experiments on real datasets show that the proposed method alleviates data sparsity and solves the problem of low recommendation accuracy.
任永功, 王瑞霞, 张志鹏. 基于社交网络能量扩散的协同过滤推荐算法[J]. 模式识别与人工智能, 2021, 34(6): 561-571.
REN Yonggong, WANG Ruixia, ZHANG Zhipeng. Collaborative Filtering Recommendation Algorithm Based on Energy Diffusion in Social Network. , 2021, 34(6): 561-571.
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