Generation and evaluation of product usage scenarios with large language models

Abstract

Recommender systems are widely used in e-commerce to enhance user convenience. Complementary recommendation is a technology that suggests combinations of products intended to improve convenience when used together. However, complementary relationships can be ambiguous, making it difficult to provide a clear definition. Therefore, we aim to develop a complementary recommendation system based on product usage scenarios using a large language model (LLM). By incorporating product usage scenarios, it is expected that the complementary recommendations will be supported by clear evidence. In this study, we conducted an experiment in which we input only the names of product categories into LLM (GPT-4o-mini), which then generated usage scenarios for these categories. The generated scenarios were subsequently manually evaluated. The experimental results confirm that approximately 85% of the generated scenarios were considered valid.