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Computer Science > Information Retrieval

arXiv:2305.04518 (cs)
[Submitted on 8 May 2023]

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

Authors:Guo Lin, Yongfeng Zhang
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Abstract: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 protocols. 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.
Subjects: Information Retrieval (cs.IR); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2305.04518 [cs.IR]
  (or arXiv:2305.04518v1 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2305.04518

Submission history

From: Guo Lin [view email]
[v1] Mon, 8 May 2023 07:28:16 UTC (1,480 KB)
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