Language Models Explain Recommendations Based on Meta-Information

Abstract

For recommender systems, the explanation of why the item was recommended to a user increases the reliability. In this work, we introduce a post-hoc method of explaining any recommender system’s output with the use of LLMs and meta information about a recommended item and user’s preferences. We try different models and introduce metrics for estimating the quality of generated explanations. The models are evaluated on three domains and then compared to analyze the ability for domain transfer.