Fusion Recommendation System Based on Collaborative Filtering and Knowledge Graph

作     者：Donglei Lu Dongjie Zhu Haiwen Du Yundong Sun Yansong Wang Xiaofang Li Rongning Qu Ning Cao Russell Higgs 

作者机构：School of Artificial IntelligenceWuxi Vocational College of Science and TechnologyWuxi214028China School of Computer Science and TechnologyHarbin Institute of TechnologyWeihai264209China School of AstronauticsHarbin Institute of TechnologyHarbin150001China Department of MathematicsHarbin Institute of TechnologyWeihai264209China School of Mathematical SciencesUniversity College DublinDublinDublin4Ireland 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2022年第42卷第9期

页      面：1133-1146页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：funded by State Grid Shandong Electric Power Company Science and Technology Project Funding under Grant no.520613200001,520613180002,62061318C002 Weihai Scientific Research and Innovation Fund(2020) 

主　　题：Fusion recommendation system knowledge graph graph embedding 

摘      要：The recommendation algorithm based on collaborative filtering is currently the most successful recommendation method. It recommends items to theuser based on the known historical interaction data of the target user. Furthermore,the combination of the recommended algorithm based on collaborative filtrationand other auxiliary knowledge base is an effective way to improve the performance of the recommended system, of which the Co-Factorization Model(CoFM) is one representative research. CoFM, a fusion recommendation modelcombining the collaborative filtering model FM and the graph embeddingmodel TransE, introduces the information of many entities and their relationsin the knowledge graph into the recommendation system as effective auxiliaryinformation. It can effectively improve the accuracy of recommendations andalleviate the problem of sparse user historical interaction data. Unfortunately,the graph-embedded model TransE used in the CoFM model cannot solve the1-N, N-1, and N-N problems well. To tackle this problem, a novel fusion recommendation model Joint Factorization Machines and TransH Model (JFMH) isproposed, which improves CoFM by replacing the TransE model with TransHmodel. A large number of experiments on two widely used benchmark data setsshow that compared with CoFM, JFMH has improved performance in terms ofitem recommendation and knowledge graph completion, and is more competitivethan multiple baseline methods.