  Social-based recommendation systems exploit the selections of friends to
combat the data sparsity on user preferences, and improve the recommendation
accuracy of the collaborative filtering strategy. The main challenge is to
capture and weigh friends' preferences, as in practice they do necessarily
match. In this paper, we propose a Neural Attention mechanism for Social
collaborative filtering, namely NAS. We design a neural architecture, to
carefully compute the non-linearity in friends' preferences by taking into
account the social latent effects of friends on user behavior. In addition, we
introduce a social behavioral attention mechanism to adaptively weigh the
influence of friends on user preferences and consequently generate accurate
recommendations. Our experiments on publicly available datasets demonstrate the
effectiveness of the proposed NAS model over other state-of-the-art methods.
Furthermore, we study the effect of the proposed social behavioral attention
mechanism and show that it is a key factor to our model's performance.
