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.
Fund:National Natural Science Foundation of China(No. 61976109), Doctoral Start-up Foundation of Natural Science Foundation of Liaoning Province(No.2020-BS-184), Dalian Science and Technology Innovation Fund(No.2018J12GX047), Dalian High Level Talents Innovation Support Project(No.2020RQ49), Dalian Key Laboratory Special Fund
Corresponding Authors:
ZHANG Zhipeng, Ph.D., lecturer. His research interests include data mining and recommender system.
About author:: REN Yonggong, Ph.D., professor. His research interests include artificial intelligence and data mining. WANG Ruixia, master student. Her research interests include artificial intelligence and data mining.
REN Yonggong,WANG Ruixia,ZHANG Zhipeng. Collaborative Filtering Recommendation Algorithm Based on Energy Diffusion in Social Network[J]. , 2021, 34(6): 561-571.
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