Extracting user needs with Chat-GPT for dialogue recommendation

• URL: http://arxiv.org/abs/2310.19303v2
• Date: Wed, 6 Dec 2023 08:55:41 GMT
• Title: Extracting user needs with Chat-GPT for dialogue recommendation
• Authors: Yugen Sato, Taisei Nakajima, Tatsuki Kawamoto, Tomohiro Takagi
• Abstract summary: Large-scale language models (LLMs) are becoming increasingly sophisticated and exhibit human-like capabilities. OpenAI's Chat-GPT has a very high inference capability as a dialogue system and the ability to generate high-quality sentences.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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