Exploring global information for session-based recommendation

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.