  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.
