Improved Representation Learning for Session-based Recommendation

• URL: http://arxiv.org/abs/2107.01516v1
• Date: Sun, 4 Jul 2021 00:57:28 GMT
• Title: Improved Representation Learning for Session-based Recommendation
• Authors: Sai Mitheran, Abhinav Java, Surya Kant Sahu and Arshad Shaikh
• Abstract summary: 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. We propose using a Transformer in combination with a target attentive GNN, which allows richer Representation Learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 using a Transformer in combination with a target attentive GNN, which allows richer Representation Learning. Our experimental results and ablation show that our proposed method outperforms the existing methods on real-world benchmark datasets.

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