Contextualizing Recommendation Explanations With LLMs: A User Study
MCML Authors
Stefan Feuerriegel
Prof. Dr.
Core PI
Abstract
Stefan Feuerriegel
Prof. Dr.
Core PI
Abstract
Large language models (LLMs) are increasingly used in recommender systems to generate natural-language explanations for contextualized recommendations. This study examines how different types of LLM-generated explanations shape user perceptions and behavioral intentions in the context of movie recommendations. In a between-subjects online experiment (N=759) and follow-up interviews (N=30), we compare LLM-generated (a) generic explanations and (b) contextualized explanations. We find that contextualized explanations better address users’ cognitive and affective needs and strengthen intention to watch the recommended movies. Interview findings further show that the effectiveness of contextualization depends on how well explanations align with users’ genre and narrative preferences. References to prior movies can improve user perceptions and intent, but excessive detail may make explanations feel redundant. The benefits of contextualization are pronounced among users with higher engagement and greater trust in the system. Together, these findings highlight the potential of LLMs to support more user-centered recommendation experiences and offer practical guidance for designing explanations that improve trust, relevance, and engagement in entertainment platforms.
inproceedings FFS26
ICWSM 2026
20th International AAAI Conference on Web and Social Media. Los Angeles, CA, USA, May 27-Jun 29, 2026.
Authors
Y. Feng • S. Feuerriegel • Y. R. ShresthaLinks
DOIResearch Area
BibTeXKey: FFS26