arXiv AI By Millend Roy, Agostino Capponi, Vineet Goyal

PREFER: Personalized Review Summarization with Online Preference Learning

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arXiv AI
Oct 7

Contrastive Learning for Aspect Representation towards Explainable Recommendation

The paper introduces CLARER, a recommendation model that fuses aspect features extracted from textual reviews with rating data to enhance recommendation accuracy and explainability. It learns user and item representations by combining rating-based features via an MLP and aspect-based features via a transformer encoder with contrastive learning. A transformer decoder then generates explanations using the combined representations, and experiments on three benchmark datasets show superior performance over baseline methods in both recommendation accuracy and explanation generation.

By Emrul Hasan, Chen Ding