Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative fi ltering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative fi ltering, became an important task in collaborative fi ltering. Traditionally, the prediction uses the user item co-occurrence data based on users’ buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative fi ltering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized. Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then, two factor-user matrices can be used to construct a so-called ‘preference dictionary’ that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.