A Collaborative Filtering Algorithm Based on SVD and Trust Factor

At present, most collaborative filtering algorithms use similarity as a criterion. In order to alleviate problems of cold start and sparsity in recommender system, a Collaborative Filtering Algorithm Combined with the Singular Value Decomposition (SVD) and Trust Factors (CFSVD-TF) is presented. Further mining data features, we use the SVD to mining data features to gain the implicit Items feature space, then the items-based similarity are computed by using the improved cosine similarity. The trust factor is integrated into the similarity space to generate the computable trust model. Finally, to evaluate the proposed CFSVD-TF approach, the accuracy of the CFSVD-TF algorithm has significantly improved than the traditional CF algorithm in MovieLens datasets.