mlsanityData to 7 Oct 2026

A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News

arXiv 2306.10702 ↗First posted 19 Jun 2023cs.IRcited by 19

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Abstract (arXiv)

Online news platforms commonly employ personalized news recommendation methods to assist users in discovering interesting articles, and many previous works have utilized language model techniques to capture user interests and understand news content. With the emergence of large language models like GPT-3 and T-5, a new recommendation paradigm has emerged, leveraging pre-trained language models for making recommendations. ChatGPT, with its user-friendly interface and growing popularity, has become a prominent choice for text-based tasks. Considering the growing reliance on ChatGPT for language tasks, the importance of news recommendation in addressing social issues, and the trend of using language models in recommendations, this study conducts an initial investigation of ChatGPT's performance in news recommendations, focusing on three perspectives: personalized news recommendation, news provider fairness, and fake news detection. ChatGPT has the limitation that its output is sensitive to the input phrasing. We therefore aim to explore the constraints present in the generated responses of ChatGPT for each perspective. Additionally, we investigate whether specific prompt formats can alleviate these constraints or if these limitations require further attention from researchers in the future. We also surpass fixed evaluations by developing a webpage to monitor ChatGPT's performance on weekly basis on the tasks and prompts we investigated. Our aim is to contribute to and encourage more researchers to engage in the study of enhancing news recommendation performance through the utilization of large language models such as ChatGPT.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jun 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 8; 2025: 3; 2026: 2.

19 papers.

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  1. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  2. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 97
  3. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 89
  4. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 72
  5. 2025A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applicationscited by 40
  6. 2024Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recencycited by 27
  7. 2024Enhancing Recommendation Diversity by Re-ranking with Large Language Modelscited by 19
  8. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  9. 2023Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Studycited by 12
  10. 2023On the Generalization of Training-based ChatGPT Detection Methodscited by 9
  11. 2024Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systemscited by 7
  12. 2024Cognitive Biases in Large Language Models for News Recommendationcited by 6
7 more
  1. 2024An Evaluation of Large Language Models in Bioinformatics Researchcited by 5
  2. 2026Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalizationcited by 5
  3. 2024Navigating User Experience of ChatGPT-based Conversational Recommender Systems: The Effects of Prompt Guidance and Recommendation Domaincited by 3
  4. 2024RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Promptscited by 2
  5. 2025Iterative Critique-Refine Framework for Enhancing LLM Personalizationcited by 1
  6. 2025CogRec: A Cognitive Recommender Agent Fusing Large Language Models and Soar for Explainable Recommendationcited by 1
  7. 2026Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curationcited by 1