Personalized Explanation

Personalized explanation research focuses on tailoring explanations of recommendations or predictions to individual users, aiming to improve user understanding and trust in AI systems. Current work leverages large language models, particularly in recommender systems, to generate these explanations, exploring different levels of detail and addressing biases inherent in training data to ensure fairness. This research is crucial for building more transparent and trustworthy AI systems across various applications, impacting fields like e-commerce, entertainment, and information retrieval.