Node representation learning with graph augmentation for sequential recommendation

Abstract

Current sequential recommender systems primarily focus on modeling users' dynamic interests over time by leveraging GNN-based approaches. However, most studies tend to overlook the impact of high-order data outside the sequence and its corresponding position information on users' interest, especially fail to maximize the collaborative signals between users or items. To alleviate the aforementioned problems, we propose a novel sequential recommendation model, called GNSR, which utilizes graph structures at various levels to enhance the node representation and ultimately improve recommendation performance. Specifically, we construct a high-dimensional continuous-time bipartite graph centered on target users and their historical sequences while preserving the interaction order. Subsequently, we create meta-path graphs through meta-path exploration. Additionally, to capture both effective high-dimensional collaborative signals and dynamic changes in users' interests throughout the sequence, we design a High-Dimensional Information Collaboration Module within GNSR, which enables a comprehensive understanding of user behavior. Finally, we conduct extensive experiments on three public datasets and demonstrate that the performance of GNSR improves by approximately 0.1% ∼ 3.2% over selected baselines.