Abstract:

With the rapid lincrease of users and commodities, user-item rating matrix has become the High-dimensional sparse matrix, causing collaborative filtering algorithm being low quality. Using the principal components analytic method to reduce the dimension of the user-item rating matrix so as to improve its sparsity. The experimental results demonstrated that compared with other collaborative filtering algorithm, recommendation quality of this algorithm is improved obviously.

Key words: dimension reduction;collaborative filtering;e-commerce