  Session-based recommendation (SBR) is a challenging task, which aims to
predict users' future interests based on anonymous behavior sequences. Existing
methods leverage powerful representation learning approaches to encode sessions
into a low-dimensional space. However, despite such achievements, all the
existing studies focus on the instance-level session learning, while neglecting
the group-level users' preference, which is significant to model the users'
behavior. To this end, we propose a novel Repeat-aware Neural Mechanism for
Session-based Recommendation (RNMSR). In RNMSR, we propose to learn the user
preference from both instance-level and group-level, respectively: (i)
instance-level, which employs GNNs on a similarity-based item-pairwise session
graph to capture the users' preference in instance-level. (ii) group-level,
which converts sessions into group-level behavior patterns to model the
group-level users' preference. In RNMSR, we combine instance-level user
preference and group-level user preference to model the repeat consumption of
users, \ie whether users take repeated consumption and which items are
preferred by users. Extensive experiments are conducted on three real-world
datasets, \ie Diginetica, Yoochoose, and Nowplaying, demonstrating that the
proposed method consistently achieves state-of-the-art performance in all the
tests.
