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.

翻译：新闻推荐系统（RS）在当前数字时代发挥着关键作用，塑造着个体获取与参与信息的方式。随着GPT和T5系列等大规模语言模型的兴起，自然语言处理（NLP）与推荐系统的融合模糊了这些领域间的界限，促使人们倾向于将推荐系统视为语言任务。ChatGPT以其用户友好的界面和日益增长的流行度而闻名，已成为广泛NLP任务的重要选择。尽管已有研究探索了ChatGPT在推荐任务中的应用，本研究通过探究其微调能力，特别是新闻领域的微调能力，开辟了新的研究方向。在本研究中，我们设计了两种不同的提示：一种将新闻推荐系统视为排序任务，另一种则针对评分任务进行定制。我们通过这两种任务框架直接获取ChatGPT的响应，评估其在新闻推荐中的性能。更重要的是，我们揭示了微调数据质量在增强ChatGPT个性化推荐能力中的关键作用，并展示了其解决推荐系统中长期存在的"冷物品"问题的潜力。我们使用微软新闻数据集（MIND）进行的实验表明，ChatGPT经过微调后实现了显著的性能提升，特别是在用户主题兴趣保持一致的情况下，将新闻推荐系统视为排序任务时效果尤为突出。本研究阐明了微调ChatGPT作为推进新闻推荐系统手段的变革潜力，提供了更有效的新闻消费体验。