SEA-GNN: Sequence Extension Augmented Graph Neural Network for Sequential Recommendation

Geyunqian Zu, Shengjie Zhao, Jin Zeng, Shilong Dong, Zixuan Chen

Abstract: Sequential recommendation aims to anticipate the next preference of users by examining their recent interactions. Recently, graph neural networks (GNNs) have been widely utilized in sequential recommendation, but existing schemes focus on interactions within individual sequences and tend to connect irrelevant items in case of insufficient historical data. In this work, we propose Sequence Extension Augmented GNN (SEA-GNN) which augments the node representation learning with inter-sequence global context aggregation while maintaining intra-sequence local preference. Specifically, we augment the graph construction with sequence extension that diversifies the item connections to exploit global context and robustify the node representation against data insufficiency. Meanwhile, we extract local preference based on the intra-sequence user-item graph to enhance the node representation with user-specific interest. Experimental results demonstrate the superiority of the proposed algorithm compared with existing schemes in recommendation performance.