Aiming at the fact that the traditional collaborative filtering algorithm cannot deeply mine user relationship and recommend new items to users, a Trust and Item Preference Collaborative Filtering (TIPCF) recommendation algorithm was proposed. Firstly, in order to mine the latent trust relationship of the users, the user reliability was gotten and the trust degree between users was quantified by analyzing user ratings. Secondly, by considering that the difference of users' preference for different target items has an effect on user similarity, user preference was added to the traditional user similarity algorithm to improve the similarity algorithm. Thirdly, the choice of nearest neighbor set was more accurate by incorporating user reliability and improved similarity. Finally, the users' preference on item attribute was used to recommend new items. Experimental results show that, compared with traditional collaborative algorithm, the Mean Absolute Error (MAE) of TIPCF was decreased by 6.7%, and the MAE of TIPCF was decreased by 10.7% when recommending new items on the Movielens dataset. TIPCF not only improves the accuracy of recommendation, but also increases the recommended probablity of new items.