LLM-generated Explanations for Recommender Systems

Abstract

Users are often confronted with situations where they have to decide in favor or against an offered item, like a book, movie, or recipe. Those suggested items are commonly determined by a recommender system, which considers personal preferences to identify relevant items. However, those systems often lack transparency and comprehensibility in revealing why a specific item is recommended. For this purpose, explanations have been added as a powerful tool to help users with their final decisions. In this paper, we present and evaluate the capabilities of a Large Language Model (LLM) to come up with high-quality explanations to further improve the support of users for three different recommendation approaches, including feature-based recommendation, collaborative filtering, and knowledge-based recommendation. We explain how an LLM can be applied to generate personalized explanations and evaluate the explanation goals in an online user study. Our findings highlight that LLM-generated explanations are highly appreciated by users as they help in the evaluation of recommended items. Furthermore, we discuss which characteristics of the LLM-based explanations were perceived positively and how those findings can be used for future research.