Empowering News Recommendation with Pre-trained Language Models

TL;DR

This paper demonstrates that integrating pre-trained language models into news recommendation systems significantly enhances their accuracy and effectiveness, as validated by experiments and deployment results on the Microsoft News platform.

Contribution

The paper introduces the application of pre-trained language models to news recommendation, showing their effectiveness over traditional methods and successful deployment in a real-world platform.

Findings

PLMs improve news recommendation performance

Deployment on Microsoft News increased clicks and pageviews

Effective in both monolingual and multilingual settings

Abstract

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.