  Session-based recommendation (SBR) is a challenging task, which aims at
recommending items based on anonymous behavior sequences. Most existing SBR
studies model the user preferences based only on the current session while
neglecting the item-transition information from the other sessions, which
suffer from the inability of modeling the complicated item-transition pattern.
To address the limitations, we introduce global item-transition information to
strength the modeling of the dynamic item-transition. For fully exploiting the
global item-transition information, two ways of exploring global information
for SBR are studied in this work. Specifically, we first propose a basic
GNN-based framework (BGNN), which solely uses session-level item-transition
information on session graph. Based on BGNN, we propose a novel approach,
called Session-based Recommendation with Global Information (SRGI), which
infers the user preferences via fully exploring global 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 session level item representation; and (ii)
Constrained-based Model (SRGI-CM), which treats the global-level
item-transition 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 consistently.
