Generalization of LLM-based reasoning methods for recommendation across item domains

Determine the extent to which zero-shot chain-of-thought prompting and reasoning-augmented fine-tuning for personalized user rating prediction with large language models generalize from the Amazon Beauty and Movies/TV categories to other content domains such as music, video games, and website articles.

Background

The paper investigates leveraging LLM reasoning for personalized recommendation, focusing on the user rating prediction task. The methods include zero-shot chain-of-thought prompting and fine-tuning with generated reasoning, evaluated on two Amazon review categories: Beauty and Movies/TV.

The authors note that recommender systems span many domains beyond those evaluated. Assessing how well these reasoning-based methods transfer to domains such as music, video games, and website articles is essential for understanding external validity and practical applicability across diverse recommendation settings.