Open AccessImprove the Collaborative Filtering Recommender System Performance by Trust Network Construction
Author(s) -
Du Yongping,
Du Xiaoyan,
Huang Liang
Publication year - 2016
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2016.05.005
Subject(s) - collaborative filtering , computer science , recommender system , similarity (geometry) , relation (database) , field (mathematics) , k nearest neighbors algorithm , pearson product moment correlation coefficient , set (abstract data type) , data mining , path (computing) , position (finance) , artificial intelligence , machine learning , mathematics , computer network , statistics , finance , pure mathematics , economics , image (mathematics) , programming language
Data sparseness brings significant challenges to the research of recommender systems. It becomes more severe for neighborhood‐based collaborative filtering. We introduce the trust relation computing of the sociology field. Instead of the traditional similarity computing method, the trust degree is integrated for the nearest neighbor selection. The trust network is constructed by the expansion of different path length, and the trust value between the users can be obtained by the trust transmission rules. To verify the effectiveness of our method, we give the experiments on different techniques for rating prediction, including Pearson based method, the User position similarity (UPS) based method and the trust with Pearson and UPS. We also give the t‐test result. The implementation of the experiment on the Epinions data set shows that the proposed method can improve the system performance significantly.