AUTHOR=Zhu Yunfeng , Yao Shuchun , Sun Xun 

TITLE=Feature Interaction Dual Self-attention network for sequential recommendation

JOURNAL=Frontiers in Neurorobotics

VOLUME=Volume 18 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2024.1456192

DOI=10.3389/fnbot.2024.1456192

ISSN=1662-5218

ABSTRACT=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.