A Neural Attention Model for Adaptive Learning of Social Friends' Preferences

Understand

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.