Local news organizations face a pressing need to increase reader engagement amid declining circulation and competition from global media [1]. Personalized news recommender systems offer a promising solution by tailoring content to user interests. However, conventional approaches often focus on user general (global) preferences and may neglect nuanced or eclectic user preferences in the local news context [1]. In this work, we propose a novel hybrid news recommender that integrates local and global preference models to enhance user engagement in local news. Building on previous research that identified the value of localized recommendation models for certain news categories [2], our approach combines the strengths of both local and non-local preference predictors within a unified framework. The proposed system adaptively combines recommendations from a local model (specialized for region-specific content) and a global model (capturing general news preferences), using ensemble strategies and multiphase training to balance the two. We evaluated the hybrid model on two datasets: (1) a large-scale synthetic news dataset based on the Syracuse local newspaper category and locality distributions [2], and (2) a Danish news dataset (EB-NeRD) labeled for local/non-local content using an LLM [3]. The results of offline experiments demonstrate that our integrated approach outperforms single-model baselines in prediction accuracy and coverage, suggesting improved personalization that can translate to higher user engagement. The findings have practical implications for news publishers, especially local outlets. Using both community-specific and general user interests, the hybrid recommender can deliver more relevant content to readers, potentially increasing retention and subscription rates. In sum, this work introduces a new direction for news recommender systems that bridges local and global models, offering a scalable solution to revitalize local news consumption through personalized user experiences.