Abstract Collaborative filteration is one of the most widely used recommendation strategies, in which data sparsity problem and expansion difficulty exist. Based on traditional user-based collaborative filtering algorithms, the trust computation is introduced into the process of recommendation. Making full use of the propagation characteristics of trust relationship under some conditions, a hybrid network composed of the user reputation-trust and the user local-trust is designed and built. And the user rating similarity is combined with trust evaluation of the hybrid network, which helps users to discover more two-dimensional similarity neighbors based on trust and interest factors. The proposed method is validated by the experiment on Epinions dataset with Mean Absolute Error (MAE) and Root Mean Square Error (RSME) as the evaluation index. The results show that compared to the traditional collaborative filtering recommendation algorithms, MAE of the proposed method increases about 6.8% and the optimal value reaches 0.7513, and the t-test results also show that the proposed method improves the performance significantly.
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2014, Vol. 27 
