Personalized news recommendation is an essential technique for online news
services. News articles usually contain rich textual content, and accurate news
modeling is important for personalized news recommendation. Existing news
recommendation methods mainly model news texts based on traditional text
modeling methods, which is not optimal for mining the deep semantic information
in news texts. Pre-trained language models (PLMs) are powerful for natural
language understanding, which has the potential for better news modeling.
However, there is no public report that show PLMs have been applied to news
recommendation. In this paper, we report our work on exploiting pre-trained
language models to empower news recommendation. Offline experimental results on
both monolingual and multilingual news recommendation datasets show that
leveraging PLMs for news modeling can effectively improve the performance of
news recommendation. Our PLM-empowered news recommendation models have been
deployed to the Microsoft News platform, and achieved significant gains in
terms of both click and pageview in both English-speaking and global markets.

本文介绍了使用预训练语言模型进行个性化新闻推荐的方法，通过离线实验结果表明，该方法在英语和全球市场上都取得了显著的点击和页面查看量的提高，关键词包括个性化新闻推荐、文本建模、预训练语言模型、新闻建模和离线实验结果。