Collaborative Filtering Recommendation Algorithm Based on Weighted Tripartite Network

被引：0
作者：
Ren Y. [1 ]
Wang N. [1 ]
Zhang Z. [1 ]
机构：
[1] School of Computer and Information Technology, Liaoning Normal University, Dalian
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Collaborative Filtering; Heat Spreading; User Preference; Weighted Tripartite Network;
D O I：
10.16451/j.cnki.issn1003-6059.202107008
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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. © 2021, Science Press. All right reserved.
引用
收藏
页码：666 / 676
页数：10
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