FASC: A Feature Aspect-Level Sentiment Consistency Framework for Explainable Recommendation Evaluation
Recent research in explainable recommendation commonly uses natural language explanations to improve transparency and user trust. However, reliably evaluating whether explanations are semantically faithful to users’ multi-dimensional preferences remains challenging. Existing methods mainly rely on text similarity metri...