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The Scientific World Journal
Volume 2014, Article ID 159594, 11 pages
http://dx.doi.org/10.1155/2014/159594
Research Article

Recommendation Based on Trust Diffusion Model

Faculty of Computer and Information Science, Southwest University, Chongqing 400715, China

Received 4 March 2014; Revised 7 May 2014; Accepted 7 May 2014; Published 9 June 2014

Academic Editor: Yolanda Blanco Fernandez

Copyright © 2014 Jinfeng Yuan and Li Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship of the trust network, thus alleviating the data sparsity problem. The probabilistic matrix factorization (PMF) is then employed to combine the users' tastes with their trusted friends' interests. We evaluate the algorithm on Flixster, Moviedata, and Epinions datasets, respectively. The experimental results show that the recommendation based on our proposed DiffTrust + PMF model achieves high performance in terms of the root mean square error (RMSE), Recall, and Measure.