Abstract
Session-based recommendation (SBR) aims to recommend items based on anonymous behavior sequences. However, most existing SBR approaches focus solely on the current session while neglecting the item-transition information from other sessions, which suffer from the inability of modeling the complicated item-transition. To address the limitations, we introduce global item-transition information to augment the modeling of item-transitions. Specifically, we first propose a basic GNN-based framework (BGNN), which solely uses session-level item-transition information. Based on BGNN, we propose a novel approach, called Session-based Recommendation with Global Information (SRGI), which infers the user preferences via fully exploring item-transitions over all sessions from two different perspectives: (i) Fusion-based Model (SRGI-FM), which recursively incorporates the neighbor embeddings of each node on global graph into the learning process of item representation; and (ii) Constrained-based Model (SRGI-CM), which treats the global-level information as a constraint to ensure the learned item embeddings are consistent with the global item-transition. Extensive experiments conducted on three popular benchmark datasets demonstrate that both SRGI-FM and SRGI-CM outperform the state-of-the-art methods.
•This paper explores high-order global information for session-based recommendation.•We leverage prototype contrastive learning to learn the global item transition.•We compare two versions of the global exploration mode in detail.•We conduct extensive experiments to compare the performance of each model.