A Neural Attention Model for Adaptive Learning of Social Friends' Preferences
This work addresses data sparsity in recommendation systems for users by leveraging social connections, but it is incremental as it builds on existing collaborative filtering with neural attention mechanisms.
The paper tackled the challenge of accurately capturing and weighing friends' preferences in social-based recommendation systems to improve recommendation accuracy, and the proposed NAS model demonstrated effectiveness over state-of-the-art methods in experiments on publicly available datasets.
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.