Double Attention-based Deformable Convolutional Network for Recommendation

Data sparsity is one of the serious problems in recommender systems, which can be tremendously alleviated by making use of informative reviews and deep learning technologies. In this paper, we propose a Double Attention-based Deformable Convolutional Network (DADCN) for recommendation. In the proposed DADCN, two parallel deformable convolutional networks, which adopt the word-level and review-level attention mechanisms, are designed to flexibly extract the deep semantic features of both users and items from reviews. The combination of two parallel deformable convolutional networks with the word-level and review-level attention mechanisms helps to capture the representative user preferences and item attributes. Extensive experimental results on four real-world datasets demonstrate that the proposed DADCN outperforms the baseline methods.