Feature-Structure Adaptive Completion Graph Neural Network for Cold-start Recommendation

The cold-start recommendation has been challenging due to the limited historical interactions for new users and new items. Recently, methods based on meta-learning and graph neural networks have been effective in this problem. However, these methods mainly focus on the missing user-item interactions in cold-start scenarios, overlooking the missing of user/item feature information, which significantly limits the quality and effectiveness of node embeddings. To address this issue, we propose a new method called Feature-Structure Adaptive Completion Graph Neural Network (FS-GNN), which is designed to tackle the cold-start problem by simultaneously addressing the missing feature and structure information in a bipartite graph composed of users and items. Specifically, we first design a trainable feature completion module that leverages the knowledge emergence abilities of large language models to enhance node embedding and mitigate the impact of missing features. Then, we incorporate a three-channel structure completion module to simultaneously complete the structures among users-users, items-items, as well as users-items. Finally, we adaptively integrate the feature and structure completion modules in an end-to-end fashion, so as to minimize cross-module interference when completing features and structures simultaneously. This generates more comprehensive and robust embeddings for users and items in recommendation tasks. Experimental results on multiple public benchmark datasets demonstrate significant improvements in our proposed FS-GNN in cold-start scenarios, outperforming or being competitive with state-of-the-art methods.