Using LLM for Profile Generation Towards Recommender System Based on Virtual Users

Abstract

This paper discusses the applicability of LLM (Large Language Model) for generating virtual user profiles. Recommender systems require users’ personal information such as their tastes and interaction histories, which could raise privacy concerns. To realize a recommendation without collecting users’ personal information, this paper proposes the concept of an explainable recommendation interface using virtual user profiles. By examining what users gave high/low ratings to target items, users can determine whether to accept/reject recommendations. This paper discusses the possibility of this concept based on the profiles generated by LLM and a pilot study with a prototype interface.