Contrastive Learning for Aspect Representation towards Explainable Recommendation

Abstract

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 preferences. To provide explanations, we train a transformer decoder, using the final representations of users and items from both rating and aspect-based features as context. Experimental results in three benchmark data sets demonstrate that our model achieves superior performance compared to baseline methods in both recommendation (accuracy) and explanation generation.

8 pages. Published in WI-IAT 2025. Best Student Paper Award

References in corpus (6)

• Deep Learning based Recommender System: A Survey and New Perspectives
• Contrastive Representation Learning: A Framework and Review
• Neural Rating Regression with Abstractive Tips Generation for Recommendation
• Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews
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