When Headlines Meet Minds: Empowering News Recommendations with Social Simulator

摘要

Personalized news recommendation aims to deliver content aligned with user interests. However, most existing methods rely on the objective textual content of news, overlooking the subjective social review that reflects how the news is socially perceived. Inspired by social constructionism, we propose Social Review-aware Recommendation (SRec), a novel framework that integrates both objective content and the social review. The latter is constructed through group deliberation modeled by an agent-based social simulator, providing structured representations of collective understandings toward news. In addition, SRec incorporates a reasoning-guided explanation module that produces interpretable rationales by aligning user preferences with the social review of news. Experimental results on the MIND-small and MIND-large datasets demonstrate that SRec improves AUC by at least 2.45% over competitive baselines. Further analysis confirms the value of the social review generated by the simulator, and shows the flexibility of SRec as a lightweight enhancement to existing recommendation systems.