earticle

영어

Traditional collaborative filtering methods just use user-item rating matrix to generate recommendations, and lead to difficult to computer the similarity because of the data sparsity. We propose a hybrid collaborative filtering algorithm combining the rating matrix and item attributes. First, we design a user similarity measurement method by computing the user’s preference to different item attributes, this approach is consistent with the true relationship between users, and also can effectively alleviate the issue of rating matrix sparse. Then, when computing the similarity of two users, we combine the Pearson correlation and the items attribute preference similarity, with a weighting coefficient “w” to balance the importance of two parts. Experiments show that this algorithm effectively solves the problem of data sparsity, and outperforms better when the sparsity is more serious, compared to the traditional CF algorithms.