title: Recommendation based on trust diffusion model.
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 F Measure.
authors.affiliation: Faculty of Computer and Information Science, Southwest University, Chongqing 400715, China.
authors.forename: Jinfeng
authors.identifier: 0000-0002-2358-2301
authors.initials: J
authors.lastname: Yuan
authors.affiliation: Faculty of Computer and Information Science, Southwest University, Chongqing 400715, China.
authors.forename: Li
authors.initials: L
authors.lastname: Li
mesh_terms: D020407:Internet / Q000592:standards*; D008962:Models, Theoretical; D035502:Trust