  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
GNN. This allows richer representations to be learnt, which translates to
empirical performance gains in comparison to a vanilla target attentive GNN.
Our experimental results and ablation show that our proposed method is
competitive with the existing methods on real-world benchmark datasets,
improving on graph-based hypotheses. Code is available at
https://github.com/The-Learning-Machines/SBR
