Abstract: Collaborative filtering algorithm suffers from data sparsity and cold start, and explicit trust is more difficult to obtain and sparse. In order to solve these problems and improve the accuracy of recommendation systems, a matrix factorization recommendation algorithm is proposed integrating implicit trust information of users. This algorithm calculates implicit trust relationship between users by using Pearson correlation coefficient and factor of trust, and then integrates implicit trust information into a matrix factorization model to predict the ratings. Experimental results show that the algorithm has better recommendation accuracy.