An optimized collaborative filtering recommendation algorithm

With the fast development of E-commerce, the magnitudes of users and commodities grow dramastically, resulting in the extremely sparse user rating data. Traditional products' similarity measurement methods perform poorly when facing sparse user rating data. Considering the extreme sparsity of the rating data, collaborative filtering algorithm based on item rating prediction is introduced. Meanwhile collaborative filtering recommendation technology does not take into account the new product whose rating is not available, although recommendation value is high. In this paper, we propose an improved strategy, which uses SVD (Singular Value Decomposition) matrix decomposition algorithm and cosine similarity to group users into clusters with common interests and further to extract the eigenvector of the commercial products to be evaluated by the users inside each group. By using BP (Back Propagation) neural network as the initial training, the proposed algorithm can predict the satisfaction of users group on new products. For those new products, the algorithm assigns higher recommending grade, and gives the priority during recommendation. Finally the results of this optimized collaborative filtering recommendation algorithm are presented. It is proven that, for new product recommendation, the performance of the new algorithm is 12% better than that of the traditional collaborative filtering recommendation algorithm.