Improving Top-N recommendations with user consuming profiles

In this work, we observe that user consuming styles tend to change regularly following some profiles. Therefore, we propose a consuming profile model to capture the user consuming styles, then apply it to improve the Top-N recommendation. The basic idea is to model user consuming styles by constructing a representative subspace. Then, a set of candidate items can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results show that the proposed model can improve the accuracy of Top-N recommendations much better than the state-of-the-art algorithms.