Takuya Nakata¹, Sinan Chen¹, Sachio Saiki² and Masahide Nakamura¹, ¹ Kobe University, Japan, ²Kochi University of Technology, Japan

Research concerning the personalization of services encompasses approaches such as machine learning and dialogue agents; however, the explainability of the recommendation process remains a challenge. Previous studies have proposed dialogue-based needs extraction systems utilizing the 6W1H need model, but extracting complex needs using simple natural language processing proved challenging. In this research, we embark on the development of an Application Programming Interface (API) that extracts user needs from natural language by leveraging the rapidly advancing Large Language Models (LLM), and on constructing a dialogue-based needs extraction system using this API. For evaluation, we conducted a verification on 100 needs with the aim of assessing the accuracy and comprehensiveness of the outputs from the needs extraction and restoration API. Through this study, it became feasible to extract needs with high accuracy and comprehensiveness from complex natural language using LLM.

Personalization, Need, Large Language Model, Natural Language Processing, Dialogue Agent