Key attribute generation from review texts based on in-context learning for recommender systems

User review texts provide valuable information for recommender systems, as they express various dimensions and perspectives regarding the experience of a user with a specific item. Consequently, many studies have proposed recommender systems based on review texts. However, because review texts typically contain a high proportion of noise that is not related to user preferences or item characteristics, existing studies that input the entire review text into the model are vulnerable to noise issues. Therefore, this study proposes a methodology for extracting key attributes based on in-context learning(ICL) to fundamentally address the noise problem in review texts. We used zero-shot, one-shot, and few-shot large language model (LLM) ICL to generate key attributes that define user preferences and item characteristics from integrated review texts, and we trained a recommender system to predict user ratings on items using the generated key attributes as new input. Our proposed research is the first to create and utilize new user and item characteristics through LLM ICL for a recommender system. Experiments demonstrate that our methodology effectively generates key attributes related to user preferences and item characteristics from review texts and achieves superior predictive performance compared to existing review-based recommender systems.