Expanding Aspect Queries into Review Sentence Fragments for Product Comparison via LLM-Generated Synthetic Reviews

This paper proposes a method for retrieving diverse real-world user reviews that refer to a specific Aspect Query representing a user’s information need. Given a short Aspect Query, such as “practicality,” the system generates a variety of Sentence Fragment queries, e.g., “*able for da*” to retrieve phrases such as “suitable for daily use” or “comfortable for daytime work.” These Sentence Fragments act as wildcard-like queries and are particularly effective in languages like Japanese, where inflection and agglutinative structures make exact keyword matching challenging. To construct such fragments, we first use a large-scale language model (LLM) to generate a large number of synthetic Aspect Query–review sentence pairs. These pairs are filtered to retain only high-quality examples, which are subsequently used to fine-tune a lightweight local LLM. The fine-tuned model generates synthetic reviews for arbitrary Aspect Queries, from which Sentence Fragments that are frequent in the synthetic reviews but rare in general reviews are extracted and used as expanded queries. A user study on a real-world review dataset demonstrates that our method enables the retrieval of diverse reviews without compromising accuracy, effectively bridging the lexical gap between abstract Aspect Queries and concrete review expressions.