A Hybrid Matrix Factorization Framework for Balancing Personalization, Diversity, and Coverage in Ad Recommendation Systems

This paper presents a comprehensive framework for addressing the inherent trade-offs among personalization, diversity, and coverage in hybrid advertisement recommendation systems. In response to the growing complexity of online advertising, the proposed system integrates collaborative filtering via Singular Value Decomposition (SVD) with content-based filtering using Non-negative Matrix Factorization (NMF), enhanced by a re-ranking mechanism based on coverage profiles. This hybrid design aims to deliver relevant, diverse, and widely distributed ads, thereby improving user engagement and fairness among content providers. To quantitatively evaluate these objectives, three novel metrics—Ad User Profile (AUP), Ad Candidate Set (ACS), and Ad Matching Degree (AMD)—are introduced. Through empirical analysis, including visualization and comparative experiments, the study demonstrates that the proposed system outperforms traditional models in achieving a more balanced recommendation outcome. Additionally, real-world case studies and optimization formulations are explored to support scalable deployment. This work contributes to the evolving landscape of recommender systems by proposing a scalable, user-centric approach that harmonizes personalization with fairness and discovery in digital advertising.