Sequential Dependency Enhanced Graph Neural Networks for Session-based Recommendations
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2021 IEEE 8th International Conference on Data Science and Advanced Analytics, DSAA 2021, 2021, 00
- Issue Date:
- 2021-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Sequential_Dependency_Enhanced_Graph_Neural_Networks_for_Session-based_Recommendations.pdf | Published version | 10.32 MB |
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Session-based recommendations (SBR) play an important role in many real-world applications, such as e-commerce and media streaming. To perform accurate session-based recommendations, it is crucial to capture both sequential dependencies over a sequence of adjacent items and complex item transitions over a set of items within sessions. Note that item transitions are not necessarily dependent on sequential dependencies, e.g., the transition from one item to the other distant item in a session is often not sequential. However, almost all the existing session-.based recommender systems (SBRS) fail to consider both kinds of information, which leads to their limited performance improvement. Aiming at this deficiency, we propose a novel sequential dependency enhanced graph neural network (SDE-GNN) to capture both sequential aependencies and item transition relations over items within sessions for more accurate next-item recommendations. Specifically, we first devise a sequential dependency learning module to capture the sequential dependencies over a sequence of adjacent items in each session. Then, we propose an item transition learning module to capture complex transitions between items. In the module, a novel residual gate and a specialized attention mechanism are integrated into gate-GNN to build an attention augmented GNN, called AU-GNN. Finally, we devise a gated fusion component to combine the learned sequential dependencies and item transitions together in preparation for the subsequent next-item recommendations. Exhaustive experiments on two public real-world data sets demonstrate the superiority of SDE-GNN over the state-of-the-art methods.
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