A Hybrid Recommendation Framework for Enhancing User Engagement in Local News

Abstract: Local news organizations face an urgent need to boost reader engagement amid declining circulation and competition from global media. Personalized news recommender systems offer a promising solution by tailoring content to user interests. Yet, conventional approaches often emphasize general preferences and may overlook nuanced or eclectic interests in local news. We propose a hybrid news recommender that integrates local and global preference models to improve engagement. Building on evidence of the value of localized models, our method unifies local and non-local predictors in one framework. The system adaptively combines recommendations from a local model, specialized in region-specific content, and a global model that captures broader preferences. Ensemble strategies and multiphase training balance the two. We evaluated the model on two datasets: a synthetic set based on Syracuse newspaper distributions and a Danish dataset (EB-NeRD) labeled for local and non-local content with an LLM. Results show our integrated approach outperforms single-model baselines in accuracy and coverage, suggesting improved personalization that can drive user engagement. The findings have practical implications for publishers, especially local outlets. By leveraging both community-specific and general user interests, the hybrid recommender can deliver more relevant content, increasing retention and subscriptions. In sum, this work introduces a new direction for recommender systems, bridging local and global models to revitalize local news consumption through scalable, personalized user experiences.