arXiv Artificial Intelligence

Contrastive Learning for Aspect Representation towards Explainable Recommendation

1 hour ago 07.10.2026 07:00 👁 2 views
Contrastive Learning for Aspect Representation towards Explainable Recommendation

Quick summary

arXiv:2610.07761v1 Announce Type: cross 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 revie

Key takeaways

  • arXiv:2610.07761v1 Announce Type: cross 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 revie

Why it matters

This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗