  Session-based recommender systems (SBRSs) predict users' next interacted
items based on their historical activities. While most SBRSs capture purchasing
intentions locally within each session, capturing items' global information
across different sessions is crucial in characterizing their general
properties. Previous works capture this cross-session information by
constructing graphs and incorporating neighbor information. However, this
incorporation cannot vary adaptively according to the unique intention of each
session, and the constructed graphs consist of only one type of user-item
interaction. To address these limitations, we propose knowledge graph-based
session recommendation with session-adaptive propagation. Specifically, we
build a knowledge graph by connecting items with multi-typed edges to
characterize various user-item interactions. Then, we adaptively aggregate
items' neighbor information considering user intention within the learned
session. Experimental results demonstrate that equipping our constructed
knowledge graph and session-adaptive propagation enhances session
recommendation backbones by 10%-20%. Moreover, we provide an industrial case
study showing our proposed framework achieves 2% performance boost over an
existing well-deployed model at The Home Depot e-platform.
