Abstract Recommender systems have been widely studied in the literature as they have real world impacts in many E-commerce platforms and social networks. Most previous systems are based on the user-item recommendation matrix, which contains users’ history recommendation activities on items. In this paper, we propose a novel predictive collaborative filtering approach that exploits both the partially observed user-item recommendation matrix and the item-based side information to produce top-N recommender systems. The proposed approach automatically identifies the most interesting items for each user from his or her non-recommended item pool by aggregating over his or her recommended items via a low-rank coefficient matrix. Moreover, it also simultaneously builds linear regression models from the item-based side information such as item reviews to predict the item recommendation scores for the users. The proposed approach is formulated as a rank constrained joint minimization problem with integrated least squares losses, for which an efficient analytical solution can be derived. To evaluate the proposed learning technique, empirical evaluations on five recommendation tasks are conducted. The experimental results demonstrate the efficacy of the proposed approach comparing to the competing methods.