Contrastive Learning for Aspect Representation towards Explainable Recommendation
In this work, we propose a novel recommendation model, CLARER (Contrastive Learning for Aspect Representation towards Explainable Recommendation) that integrates aspect features learned from textual reviews with rating information to improve the accuracy and explainability of recommendations. Our proposed framework learns user and item representations by combining rating-based features and aspect-based features from reviews. Specifically, rating-based features are learned through a multi-layer perceptron (MLP) model, while aspect-specific review representations are learned using a transformer encoder to capture the semantic information and contrastive learning to better distinguish user p...
| Authors | Emrul Hasan, Chen Ding |
|---|---|
| Primary category | cs.IR |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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