  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.
