The collaborative filtering recommendation algorithm is the widely used technology in the personalized e-commerce recommendation system. However, the traditional recommendation algorithm neglected trading hours and product pricing when recommended products, which led to the lower quality recommended. To solve this problem, a collaborative filtering model considering time and price factors is proposed in this paper, and experiments show that the improvement of algorithm is most effective when time and price factors are taken into account in the calculation of Pearson correlation coefficient.