Papers › Introducing Self-Attention to Target Attentive Graph Neural Networks

Introducing Self-Attention to Target Attentive Graph Neural Networks

4 Jul 2021arXiv:2107.01516archive 2025-07-28

Sai Mitheran, Abhinav Java, Surya Kant Sahu, Arshad Shaikh

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

Paper PDF Code

Code

The-Learning-Machines/SBR officialmentioned in papermentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Recommendation Systems Representation Learning Session-Based Recommendations

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Session-Based Recommendations	Diginetica	TAGNN++	Hit@20	51.86	#8 of 13	Archive leaderboard	report
Session-Based Recommendations	Diginetica	TAGNN++	MRR@20	17.93	#8 of 13	Archive leaderboard	report
Session-Based Recommendations	yoochoose1/64	TAGNN++	HR@20	71.91	#5 of 11	Archive leaderboard	report
Session-Based Recommendations	yoochoose1/64	TAGNN++	MRR@20	31.57	#5 of 11	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections