Collaborative Filtering Algorithm Based on Trusted Similarity

In order to solve the problem of information overload, a large number of personalized recommendation algorithms merged. Data sparsity is one of the difficult problems in these algorithms. Aiming at this, a novel collaborative filtering algorithm which introduces the trust into the traditional collaborative filtering recommendation algorithm is proposed in this paper. The proposed algorithm first measures the comprehensive trust by weighting the direct trust and the indirect trust, then obtains the similarity using Pearson model, and calculates the trusted similarity for prediction at last. To verify the performance of the proposed algorithm, the Mean Absolute Error (MAE) between the proposed algorithm with adaptive coordination factor and the traditional collaborative filtering recommendation algorithm with empirical factor is compared. The result shows that the proposed algorithm has better prediction accuracy.