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.
Highlights
With the rapidly growing amount of information available on the WWW, recommender systems become a popular way to help users select relevant information on the Internet
Trust-based recommender systems utilize a social network augmented with trust ratings, known as a trust network, to generate recommendations for users based on people they trust
Aiming at effectively overcoming the above limitations and modeling recommender systems more accurately this paper presents a new recommendation method based on trust diffusion mechanism (DiffTrust + probabilistic matrix factorization (PMF))
Summary
With the rapidly growing amount of information available on the WWW, recommender systems become a popular way to help users select relevant information on the Internet. Despite its popularity and success, the performance of CF is significantly limited by the “data sparsity” and “cold start” [3, 5] In view of these limitations, many scholars have recently integrated trust relationship among users into the recommendation system [6,7,8,9,10]. Most recommendation algorithms [12,13,14] are based on the traditional probabilistic matrix factorization model and fusion between user-item matrix and social relationships by sharing a potential low dimensional user characteristic matrix. These methods can only learn few effective characteristics. This disadvantage causes lack of interpretability in the model, and affects the quality of the recommendation
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