Papers › Star Graph Neural Networks for Session-based Recommendation
Star Graph Neural Networks for Session-based Recommendation
Zhiqiang Pan, Fei Cai, Wanyu Chen, Honghui Chen, Maarten de Rijke
Session-based recommendation is a challenging task. Without access to a user's historical user-item interactions, the information available in an ongoing session may be very limited. Previous work on session-based recommendation has considered sequences of items that users have interacted with sequentially. Such item sequences may not fully capture complex transition relationship between items that go beyond inspection order. Thus graph neural network (GNN) based models have been proposed to capture the transition relationship between items. However, GNNs typically propagate information from adjacent items only, thus neglecting information from items without direct connections. Importantly, GNN-based approaches often face serious overfitting problems. We propose Star Graph Neural Networks with Highway Networks (SGNN-HN) for session-based recommendation. The proposed SGNN-HN applies a star graph neural network (SGNN) to model the complex transition relationship between items in an ongoing session. To avoid overfitting, we employ highway networks (HN) to adaptively select embeddings from item representations. Finally, we aggregate the item embeddings generated by the SGNN in an ongoing session to represent a user's final preference for item prediction. Experiments on two public benchmark datasets show that SGNN-HN can outperform state-of-the-art models in terms of P@20 and MRR@20 for session-based recommendation.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Session-Based Recommendations | Diginetica | SGNN-HN | Hit@20 | 55.67 | #2 of 13 | Archive leaderboard | report |
| Session-Based Recommendations | Diginetica | SGNN-HN | MRR@20 | 19.45 | #2 of 13 | Archive leaderboard | report |
| Session-Based Recommendations | yoochoose1/4 | SGNN-HN | HR@20 | 72.85 | #2 of 4 | Archive leaderboard | report |
| Session-Based Recommendations | yoochoose1/4 | SGNN-HN | MRR@20 | 32.55 | #2 of 4 | Archive leaderboard | report |
| Session-Based Recommendations | yoochoose1/64 | SGNN-HN | HR@20 | 72.06 | #1 of 11 | Archive leaderboard | report |
| Session-Based Recommendations | yoochoose1/64 | SGNN-HN | MRR@20 | 32.61 | #1 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
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