Sparks of Artificial General Recommender (AGR): Experiments with ChatGPT

This study investigates the feasibility of developing an Artificial General Recommender (AGR), facilitated by recent advancements in Large Language Models (LLMs). An AGR comprises both conversationality and universality to engage in natural dialogues and generate recommendations across various domai...

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Bibliographic Details
Published inAlgorithms Vol. 16; no. 9; p. 432
Main Authors Lin, Guo, Zhang, Yongfeng
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2023
Subjects
Artificial General Recommender
Chatbots
ChatGPT
conversational recommender system
Feasibility studies
Feedback
Large language models
Metadata
multi-domain recommender system
multi-task recommender system
Principles
Protocol
Recommender systems
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Summary:This study investigates the feasibility of developing an Artificial General Recommender (AGR), facilitated by recent advancements in Large Language Models (LLMs). An AGR comprises both conversationality and universality to engage in natural dialogues and generate recommendations across various domains. We propose ten fundamental principles that an AGR should adhere to, each with its corresponding testing protocol. We proceed to assess whether ChatGPT, a sophisticated LLM, can comply with the proposed principles by engaging in recommendation-oriented dialogues with the model while observing its behavior. Our findings demonstrate the potential for ChatGPT to serve as an AGR, though several limitations and areas for improvement are identified.
ISSN:1999-4893
1999-4893
DOI:10.3390/a16090432