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Exploring Fine-tuning ChatGPT for News Recommendation

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arxiv 2311.05850 v1 pith:ABBEUANL submitted 2023-11-10 cs.IR

Xinyi Li , Yongfeng Zhang , Edward C Malthouse This is my paper · ORCID
classification cs.IR
keywords newschatgptfine-tuningrecommendationtasklanguagetaskspivotal
verification ladder T0 review T1 audit T2 compute T3 formal
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News recommendation systems (RS) play a pivotal role in the current digital age, shaping how individuals access and engage with information. The fusion of natural language processing (NLP) and RS, spurred by the rise of large language models such as the GPT and T5 series, blurs the boundaries between these domains, making a tendency to treat RS as a language task. ChatGPT, renowned for its user-friendly interface and increasing popularity, has become a prominent choice for a wide range of NLP tasks. While previous studies have explored ChatGPT on recommendation tasks, this study breaks new ground by investigating its fine-tuning capability, particularly within the news domain. In this study, we design two distinct prompts: one designed to treat news RS as the ranking task and another tailored for the rating task. We evaluate ChatGPT's performance in news recommendation by eliciting direct responses through the formulation of these two tasks. More importantly, we unravel the pivotal role of fine-tuning data quality in enhancing ChatGPT's personalized recommendation capabilities, and illustrates its potential in addressing the longstanding challenge of the "cold item" problem in RS. Our experiments, conducted using the Microsoft News dataset (MIND), reveal significant improvements achieved by ChatGPT after fine-tuning, especially in scenarios where a user's topic interests remain consistent, treating news RS as a ranking task. This study illuminates the transformative potential of fine-tuning ChatGPT as a means to advance news RS, offering more effective news consumption experiences.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models

    cs.IR 2026-01 reject novelty 7.0 of 10

    BEAR is a cheap token-level top-B regularizer for LLM-based recommendation, but its central claim that this condition is necessary for beam-search survival is incorrect.

  2. Revisiting Language Models in Neural News Recommender Systems

    cs.IR 2025-01 conditional novelty 5.0 of 10

    Larger language models as news encoders do not consistently improve recommendation accuracy, but they do improve performance for cold-start users, at higher fine-tuning and compute cost.

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