Empowering News Recommendation with Pre-trained Language Models
This work addresses the need for more accurate news modeling in online news services, though it is incremental as it adapts existing PLMs to a new application area.
The authors tackled the problem of personalized news recommendation by applying pre-trained language models (PLMs) to better model news texts, resulting in improved performance on monolingual and multilingual datasets and significant gains in click and pageview metrics after deployment on the Microsoft News platform.
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.