LAUSR

Abstract Neighborhood-based collaborative filtering is a method of high significance among recommender systems, with advantages of simplicity and justifiability. However, recently it is receiving less popularity due to its low prediction accuracy in contrast with model-based collaborative filtering systems, but model-based methods also suffer from a drawback worthy of attention that is they cannot effectively explain the reason behind their estimation. In order to develop a system with both high accuracy and justifiability, we propose a novel neighborhood-based collaborative filtering method inspired by the natural mechanism of attention. Our method can adaptively find neighborhood items to the prediction in user history without any pre-defined function with respect item correlations. Then the estimation are made based on these relationships. Experiments on several benchmarks are carried out to verify the performance of the proposed method, and the result shows that our method beats all previous state-of-the-art methods on MovieLens 10M and Netflix in addition to being able to justify the prediction obtained.