Polarization occurs within society's networks when highly connected groups form with weak intergroup links, leading to echo chambers and filter bubbles. These phenomena hinder exposure to diverse viewpoints, posing significant challenges to democracy and societal welfare. Despite extensive research on measuring and mitigating social network polarization, the effectiveness of existing metrics remains largely uncharted. This study reevaluates these metrics and recommender system-based reduction strategies, pinpointing inherent limitations. It highlights key factors influencing polarization and adopts a design science research approach to craft a recommender system-based model for reducing polarization in online networks, recognizing its complex nature.