Abstract The over-concentration of recommendation results of user-based collaborative filtering algorithm on popular items causes the lack of diversity, novelty and coverage. Aiming at this problem, a collaborative filtering recommendation algorithm based on weighted tripartite network is proposed. Based on sparse analysis data and little additional information, tags are introduced to reflect user interests and item attributes simultaneously. Ternary relationships among users, items and tags are utilized to construct a tripartite network.The user preference is obtained by projecting the tripartite network to the one-mode network, and a tripartite network model weighted by user preference is constructed. According to the heat spreading method, resources are redistributed on the weighted tripartite network to find more similarity relationships. The standard framework of collaborative filtering is applied for prediction and recommendation. Experiments on real datasets show that the proposed method mines long-tail items better and realizes personalized recommendations.
Fund:National Natural Science Foundation of China(No.61976109), Doctoral Startup Project 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), Special Fund of Dalian Key Laboratory
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 Ningjing, master student. Her research interests include artificial intelligence and data mining.
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