Abstract
Trust-aware recommender system (TARS) can provide more relevant recommendation and more accurate rating predictions than the traditional recommender system by taking the trust factors into consideration, yet currently only static trust is modeled in these systems. In this paper, we propose to integrate the social network analysis based dynamic trust model with context-aware matrix factorization into a new dynamic trust-based context-aware matrix factorization(DTCMF) to fully capture the dynamic of trust. Evaluations based on a real dataset and three semi-synthetic datasets demonstrate that our approaches can not only ensure the stability of the system, but also leads to more accurate recommendations.
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