In today’s digital ecosystem, users are overwhelmed by heterogeneous streams of information from social networks, e-commerce platforms, and streaming services, and the ability to filter this flow and deliver personalized content has become a key differentiator for digital platforms. Recommendation systems form the core of modern personalization technologies, shaping how users interact with digital platforms and influencing engagement, retention, and overall growth. Yet older approaches still face persistent obstacles. Collaborative filtering often performs well once a solid history of interactions exists, but its accuracy quickly drops when data are sparse or when new users and items appear. Graph-based methods such as Personalized PageRank capture the structure linking users and items, yet they demand more computation and scale less easily. This paper presents a hybrid cascade that combines collaborative filtering with a Personalized PageRank re-ranker to balance accuracy, coverage, and scalability. The system is built in Java with Spring Boot, PostgreSQL, and Redis in a modular design that supports real-time operation. Collaborative filtering first proposes candidate recommendations, then a graph-diffusion pass on a bipartite user–item graph orders them. On a social media analytics dataset, the hybrid outperforms either method alone, achieving a precision of 0.75, a recall of 0.68, and a mean absolute error of 0.10. These results confirm the effectiveness of combining similarity-driven and structure-aware techniques to overcome data sparsity and improve robustness. The contribution of this study lies in the design of a hybrid architecture validated on real-world data with measurable performance gains and in pointing toward future extensions with deep learning models and applications at an industrial scale.