Self-attention mechanism is primarily designed to capture the correlation (interaction) between any two objects in a sequence. Inspired by self-attention's success in many NLP tasks, some researchers have employed self-attention in sequential recommendation to refine user representations by capturing the correlations between the historical interacted items of a user. However, the user representations in previous self-attention based models are not flexible enough since the self-attention is only applied on user side, restricting performance improvement. In this paper, we propose a deep recommendation model with feature-level self-attention, namely SAFrec, which exhibits enhanced recommendation performance mainly due to its two advantages. The first one is that SAFrec employs self-attention mechanism on user side and item side simultaneously, to co-refine user representations and item representations. The second one is that, SAFrec leverages item features distilled from open knowledge graphs or websites, to represent users and items on fine-grained level (feature-level). Thus the correlations between users and items are discovered sufficiently. The extensive experiments conducted over two real datasets (NetEase music and Book-Crossing) not only demonstrate SAFrec's superiority on top-n recommendation over the state-of-the-art deep recommendation models, but also validate the significance of incorporating self-attention mechanism and feature-level representations.

Although many researchers of recommender systems have noted that encoding user-item interactions based on DNNs promotes the performance of collaborative filtering, they ignore that embedding the latent features collected from external sources, e.g., knowledge graphs (KGs), is able to produce more precise recommendation results. Furthermore, CF-based models are still vulnerable to the scenarios of sparse known user-item interactions. In this paper, towards movie recommendation, we propose a novel knowledge-enhanced deep recommendation framework incorporating GAN-based models to acquire robust performance. Specifically, our framework first imports various feature embeddings distilled not only from user-movie interactions, but also from KGs and tags, to constitute initial user/movie representations. Then, user/movie representations are fed into a generator and a discriminator simultaneously to learn final optimal representations through adversarial training, which are conducive to generating better recommendation results. The extensive experiments on a real Douban dataset demonstrate our framework's superiority over some state-of-the-art recommendation models, especially in the scenarios of sparse observed user-movie interactions.