mlsanityData to 7 Oct 2026

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

arXiv 2305.04518 ↗First posted 8 May 2023cs.IR cs.CL cs.LGcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 7 of its 27 references that are in the map. First posted on arXiv 8 May 2023.

Builds on

7 of 27 references.

References · 7

  1. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  2. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  3. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  4. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
  5. 2020A Survey on Conversational Recommender Systemscited by 142
  6. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  7. 2018Conversational Recommender Systemcited by 115

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 8; 2024: 1; 2025: 3; 2026: 1.

13 papers.

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  1. 2023A Survey on Large Language Models for Recommendationcited by 378
  2. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  3. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  4. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  5. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  6. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 72
  7. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  8. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  9. 2024A Survey of Generative Search and Recommendation in the Era of Large Language Modelscited by 14
  10. 2023Large Language Models are Not Stable Recommender Systemscited by 13
  11. 2025Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentationcited by 4
  12. 2025Catalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendationcited by 1
1 more
  1. 2026Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillationcited by 0