Leveraging Feature Interaction for Modeling User-Item Interaction in Recommender Systems

Challenges in recent recommender systems include how to model high-order feature interaction and how to exploit user-item interaction, particularly for neural network-based recommender systems. While previous approaches have focused only on one aspect, this paper attempts to address both simultaneously by extracting augmented embeddings for users and items with feature interaction and modeling user-item interaction using graph neural networks. Real-world experimental results show that the proposed method outperforms state-of-the-art methods considering one type of interaction.