Session-based Interactive Recommendation via Deep Reinforcement Learning
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE International Conference on Data Mining (ICDM), 2024, pp. 1319-1324
- Issue Date:
- 2024-02-05
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Deep reinforcement learning DRL has shown promise in solving intractable challenges in interactive recommendation systems In DRL based interactive recommendation state modeling is crucial for well capturing users continuous interaction behaviors with shopping systems A user s multiple continuous interactions in a given time period e g the time from login to log out naturally constitute a session However existing studies often overlook such valuable session structure and characteristics and instead simply treat them as sequences As a result they are not able to capture the complex transitions over users interactions within or between sessions leading to significant information loss To bridge this significant gap in this paper we propose Session based Interactive Recommendation with Graph Neural Networks SIR GNN SIR GNN models interaction data as sessions and employs novel graph neural networks to capture rich transition patterns among interactions Specifically a novel 3 level transition module is well designed to effectively capture common patterns from all sessions intra session transitions and adjacent item transitions respectively followed by an attention based gated graph neural network to model the state representation for SIR well Extensive experiments on 3 real world benchmark datasets demonstrate the superiority of SIR GNN over state of the art baselines and the rationality of our design in SIR GNN
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