A conditional random field recommendation method based on tripartite graph

作     者：Wang, Xin Han, Lixin Li, Jingxian Yan, Hong 

作者机构：Hohai Univ Sch Comp & Informat Nanjing 211106 Peoples R China Jinling Inst Technol Sch Software Engn Nanjing 211169 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China 

出 版 物：《EXPERT SYSTEMS WITH APPLICATIONS》 (专家系统及其应用)

年 卷 期：2024年第238卷第PartC期

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA) , Hong Kong Research Grants Council City University of Hong Kong 

主　　题：Recommendation algorithm Graph-based recommendation Conditional random field Data sparsity Tripartite graph Diversity 

摘      要：Recommender System (RS) has generated widespread attention with the aim of expanding different items. Among graph-based recommendation methods, the tripartite graph can better manage data sparsity and cold start, while improving the metrics of various recommendations such as recall, precision, and diversity. Existing tripartite graph-based methods encounter numerous challenges, including mitigating data sparsity, improving diversity, and capturing potential user preferences via social relations. To address these challenges, a Conditional Random Field based on Tripartite Graph (CRF-TG) is proposed. The tripartite graph consists of the user, item, and trust level. The method can mine potentially similar users, create probabilistic models based on TG, and uncover potential user preferences. Moreover, to mine the users with similar preferences outside the social relationship, the random walk method is used to test CRF-TG. Experiments are designed to verify the validity of CRF-TG. Compared to the others considered methods, CRF-TG gives a 15% increase on average in performance indicators such as diversity, recall, and F1.