Feature-Aware Attentive Variational Auto-Encoder for Top-N Recommendation

Personalized recommendation has become increasingly pervasive due to its great commercial value in business. Deep neural networks can automatically exvacate the behavior patterns from the historical interaction records, which has achieved excellent results in related tasks. Among them, the variational auto-encoders have been shown to be superior for learning to rank and recommendation on massive data. However, prior work neglects the association between user behavior and side information, which affects the quality of recommendation services to some extent. In this paper, we propose a feature-aware attentive variational auto-encoder for top-N recommendation. The attention mechanism is utilized to capture the relationship between user's representation and side information through a sub network, balancing the fusion weight of attributes in the main network. In addition, this method tries to construct combination of features in the high-dimensional embedding space, helping mining the promotion of side information at a finer scale. Experiments conducted on real-world datasets demonstrate the effectiveness over the state-of-art methods.