GCACL-Rec: A study on conversational recommendation via global context-aware and multi-view contrastive adversarial joint learning
Xianghui Li, Xiaowen Liu, Xinhuan Chen, Ming Ma, Ping Xiang, Ping Xiang, Ping Xiang
TL;DR
This paper introduces GCACL-Rec, a new recommendation system that improves session-based recommendations by considering user behavior across multiple sessions and using advanced learning techniques.
Contribution
The novel contribution is the integration of global context-aware modeling and multi-view contrastive adversarial learning for session-based recommendations.
Findings
GCACL-Rec outperforms existing methods on benchmark datasets like Diginetica, Tmall, and RetailRocket.
The model effectively captures cross-session dependencies and improves recommendation accuracy through its hybrid prediction structure.
Abstract
Session-based recommendation (SBR) aims to provide personalized recommendations based on anonymous user click sequences. Although existing methods have achieved notable progress, most focus solely on user preferences within a single session, overlooking item transitions across sessions, which limits their ability to model complex behavior patterns. To address this, we propose GCACL-Rec, a model that enhances dynamic modeling by incorporating global item transition information. It constructs a multi-scale graph structure using Multi-scale graph neural networks (MSGNN) and introduces a relative multi-head attention mechanism (RMA) to enhance cross-session dependency modeling. In addition, a multi-view contrastive-adversarial joint learning strategy (MPACL) is adopted to distinguish better relevant from irrelevant information and extract user intent more effectively. During prediction, we…
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Taxonomy
TopicsRecommender Systems and Techniques · Emotion and Mood Recognition · Topic Modeling