The inherent weakness of the data on user ratings collected from the web, such as sparsity and cold-start, has limited the data analysis capability and prediction accuracy in recommender systems. To alleviate this problem, trust has been incorporated in collaborative filtering approaches with encouraging experimental results. In this paper, we propose a computational model for trust-based CF with three different methods to infer trust in a social network, based on a detailed data analysis of hotel dataset. We apply these methods on users ratings of hotels and show its feasibility by comparing the testing results with conventional CF algorithm using evaluation metrics Mean absolute error (MAE) and prediction coverage. Our experimental results indicate that the use of trust can improve prediction accuracy if the definition of trust is reasonable enough.
展开▼
