Extracting user needs with Chat-GPT for dialogue recommendation

TL;DR

This paper explores using ChatGPT as an integrated dialogue and recommendation system to enhance interactive AI applications, leveraging its high inference and language generation capabilities.

Contribution

It demonstrates constructing a unified dialogue and recommendation system using ChatGPT, combining conversational and recommendation functionalities in a novel way.

Findings

ChatGPT can effectively serve as both dialogue and recommendation system

The integrated system improves user interaction quality

High-quality sentence generation enhances recommendation relevance

Abstract

Large-scale language models (LLMs), such as ChatGPT, are becoming increasingly sophisticated and exhibit human-like capabilities, playing an essential role in assisting humans in a variety of everyday tasks. An important application of AI is interactive recommendation systems that respond to human inquiries and make recommendations tailored to the user. In most conventional interactive recommendation systems, the language model is used only as a dialogue model, and there is a separate recommendation system. This is due to the fact that the language model used as a dialogue system does not have the capability to serve as a recommendation system. Therefore, we will realize the construction of a dialogue system with recommendation capability by using OpenAI's Chat-GPT, which has a very high inference capability as a dialogue system and the ability to generate high-quality sentences, and verify the effectiveness of the system.