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Introducing Self-Attention to Target Attentive Graph Neural Networks
[article]
2022
arXiv
pre-print
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
arXiv:2107.01516v3
fatcat:3fwwcnkc7vavtbe3wenumx6xgm