Enhancing explainable rating prediction through annotated macro concepts
Generating recommendation reasons for recommendation results is a long-standing
problem because it is challenging to explain the underlying reasons for recommending an …
problem because it is challenging to explain the underlying reasons for recommending an …
Lane: Logic alignment of non-tuning large language models and online recommendation systems for explainable reason generation
Aligning Explanations for Recommendation with Rating and Feature via Maximizing Mutual Information
Providing natural language-based explanations to justify recommendations helps to improve
users' satisfaction and gain users' trust. However, as current explanation generation …
users' satisfaction and gain users' trust. However, as current explanation generation …
TEARS: Textual Representations for Scrutable Recommendations
Traditional recommender systems rely on high-dimensional (latent) embeddings for
modeling user-item interactions, often resulting in opaque representations that lack …
modeling user-item interactions, often resulting in opaque representations that lack …