Wednesday, May 14, 2014
NEW PAPER "Recommendation Based on Trust Diffusion Model"
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 trusts 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 combines 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 FMeasure.
Personalized recommendation adopts knowledge discovery techniques such as data mining and machine learning to discover user interests according to user behavior and then to make recommendations. Typically, collaborative Filtering (CF) is the most successful and widely used recommendation technique. CF makes recommendation according to the assumption that users who have the similar performances would like to choose the similar items. Despite its popularity and success, the performance of CF is significantly limited by "data sparsity" and "cold start" weakpoints.
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.
Please read also:
PAPER: "How to Improve Multi-Agent Recommendations Using Data from Social Networks?"
Personality based recommenders are the next generation of recommender systems. http://onlinedatingsoundbarrier.blogspot.com.ar/2014/03/new-papers-recommender-systems.html
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