Social Network and Tag Sources Based Augmenting Collaborative Recommender System

Tinghuai MA  Jinjuan ZHOU  Meili TANG  Yuan TIAN  Abdullah AL-DHELAAN  Mznah AL-RODHAAN  Sungyoung LEE  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E98-D   No.4   pp.902-910
Publication Date: 2015/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2014EDP7283
Type of Manuscript: PAPER
Category: Office Information Systems, e-Business Modeling
Keyword: 
recommender system,  collaborative filtering,  social tagging,  social network,  

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Summary: 
Recommender systems, which provide users with recommendations of content suited to their needs, have received great attention in today's online business world. However, most recommendation approaches exploit only a single source of input data and suffer from the data sparsity problem and the cold start problem. To improve recommendation accuracy in this situation, additional sources of information, such as friend relationship and user-generated tags, should be incorporated in recommendation systems. In this paper, we revise the user-based collaborative filtering (CF) technique, and propose two recommendation approaches fusing user-generated tags and social relations in a novel way. In order to evaluate the performance of our approaches, we compare experimental results with two baseline methods: user-based CF and user-based CF with weighted friendship similarity using the real datasets (Last.fm and Movielens). Our experimental results show that our methods get higher accuracy. We also verify our methods in cold-start settings, and our methods achieve more precise recommendations than the compared approaches.