Paper Info
Title
Exploiting homophily-based implicit social network to improve recommendation performance
Abstract
Social information between users has been widely used to improve the traditional Recommender System in many previous works. However, in many websites such as Amazon and eBay, there is no explicit social graph that can be used to improve the recommendation performance. Hence in this work, in order to make it possible to employ social recommendation methods in those non-social information websites, we propose a general framework to construct a homophily-based implicit social network by utilizing both the rating and comments of items given by the users. Our scalable framework can be easily extended to enhance the performance of any recommender systems without social network by replacing the homophily-based implicit social relation definition. We propose four methods to extract and analyze the implicit social links between users, and then conduct the experiments on Amazon dataset. Experimental results show that our proposed methods work better than traditional recommendation methods without social information.
Year
DOI
Venue
2014
10.1109/IJCNN.2014.6889743
IJCNN
Keywords
Field
DocType
amazon dataset,implicit social link analysis,recommender system,recommendation performance improvement,implicit social link extraction,recommender systems,ebay,social networking (online),social recommendation methods,social information,nonsocial information websites,homophily-based implicit social network,vectors,motion pictures,correlation,linear programming
Social relation,Recommender system,Internet privacy,Social graph,Social network,Information retrieval,Homophily,Computer science,Artificial intelligence,Social information,Machine learning,Scalability
Conference
ISSN
Citations 
PageRank 
2161-4393
1
0.35
References 
Authors
15
6
Name
Order
Citations
PageRank
Tong Zhao122014.25
Junjie Hu211220.50
Pinjia He323912.63
Hang Fan410.35
Michael R. Lyu510985529.03
Irwin King66751325.94