Introducing Self-Attention to Target Attentive Graph Neural Networks. (arXiv:2107.01516v3 [cs.IR] UPDATED)

Session-based recommendation systems suggest relevant items to users by
modeling user behavior and preferences using short-term anonymous sessions.
Existing methods leverage Graph Neural Networks (GNNs) that propagate and
aggregate information from neighboring nodes i.e., local message passing. Such
graph-based architectures have representational limits, as a single sub-graph
is susceptible to overfit the sequential dependencies instead of accounting for
complex transitions between items in different sessions. We propose a new
technique that leverages a Transformer in combination with a target attentive …

arxiv attention graph graph neural networks networks neural networks