Title :
Social Recommendation Based on Multi-relational Analysis
Author :
Jian Chen ; Guanliang Chen ; Haolan Zhang ; Jin Huang ; Gansen Zhao
Author_Institution :
Sch. of Software Eng., South China Univ. of Technol., Guangzhou, China
Abstract :
Social recommendation methods, often taking only one kind of relationship in social network into consideration, still faces the data sparsity and cold-start user problems. This paper presents a novel recommendation method based on multi-relational analysis: first, combine different relation networks by applying optimal linear regression analysis, and then, based on the optimal network combination, put forward a recommendation algorithm combined with multi-relational social network. The experimental results on Epinions dataset indicate that, compared with existing algorithms, can effectively alleviate data sparsity as well as cold-start issues, and achieve better performance.
Keywords :
information retrieval; recommender systems; regression analysis; social networking (online); Epinions dataset; cold-start user problem; data sparsity; multirelational analysis; multirelational social network; optimal linear regression analysis; optimal network combination; recommendation algorithm; relation network; social recommendation method; multi-relation social network; regression analysis; social recommendation;
Conference_Titel :
Web Intelligence and Intelligent Agent Technology (WI-IAT), 2012 IEEE/WIC/ACM International Conferences on
Conference_Location :
Macau
Print_ISBN :
978-1-4673-6057-9
DOI :
10.1109/WI-IAT.2012.222