Feature Interaction Dual Self-attention network for sequential recommendation

Combining item feature information helps extract comprehensive sequential patterns, thereby improving the accuracy of sequential recommendations. However, existing methods usually combine features of each item using a vanilla attention mechanism. We argue that such a combination ignores the interactions between features and does not model integrated feature representations. In this study, we propose a novel Feature Interaction Dual Self-attention network (FIDS) model for sequential recommendation, which utilizes dual self-attention to capture both feature interactions and sequential transition patterns. Specifically, we first model the feature interactions for each item to form meaningful higher-order feature representations using a multi-head attention mechanism. Then, we adopt two independent self-attention networks to capture the transition patterns in both the item sequence and the integrated feature sequence, respectively. Moreover, we stack multiple self-attention blocks and add residual connections at each block for all self-attention networks. Finally, we combine the feature-wise and item-wise sequential patterns into a fully connected layer for the next item recommendation. We conduct experiments on two real-world datasets, and our experimental results show that the proposed FIDS method outperforms state-of-the-art recommendation models.