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

arXiv: https://arxiv.org/abs/2306.10702 · HTML: https://mlsanity.com/paper/2306.10702/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2023.

## Built on by (most cited first, 19 of 19)

- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://mlsanity.com/paper/2404.11457/) (2024)
- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [Large Language Models for Generative Recommendation: A Survey and Visionary Discussions](https://mlsanity.com/paper/2309.01157/) (2023)
- [A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications](https://mlsanity.com/paper/2503.17003/) (2025)
- [Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency](https://mlsanity.com/paper/2401.10545/) (2024)
- [Enhancing Recommendation Diversity by Re-ranking with Large Language Models](https://mlsanity.com/paper/2401.11506/) (2024)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Study](https://mlsanity.com/paper/2311.04199/) (2023)
- [On the Generalization of Training-based ChatGPT Detection Methods](https://mlsanity.com/paper/2310.01307/) (2023)
- [Cognitive Biases in Large Language Models for News Recommendation](https://mlsanity.com/paper/2410.02897/) (2024)
- [Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systems](https://mlsanity.com/paper/2401.04057/) (2024)
- [An Evaluation of Large Language Models in Bioinformatics Research](https://mlsanity.com/paper/2402.13714/) (2024)
- [Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization](https://mlsanity.com/paper/2602.15028/) (2026)
- [Navigating User Experience of ChatGPT-based Conversational Recommender Systems: The Effects of Prompt Guidance and Recommendation Domain](https://mlsanity.com/paper/2405.13560/) (2024)
- [RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts](https://mlsanity.com/paper/2406.06577/) (2024)
- [Iterative Critique-Refine Framework for Enhancing LLM Personalization](https://mlsanity.com/paper/2510.24469/) (2025)
- [CogRec: A Cognitive Recommender Agent Fusing Large Language Models and Soar for Explainable Recommendation](https://mlsanity.com/paper/2512.24113/) (2025)
- [Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation](https://mlsanity.com/paper/2604.15937/) (2026)

## 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.