Hybrid Recommender Systems: Bridging Accuracy Gaps with Quantitative Evidence

Recommender systems have become a staple in the digital ecosystems such as in personalizing contents, products, and services, driving user engagement. The present study discusses the hybrid recommender system, a paradigm based on the combination of the strengths of multiple recommender systems techniques, to overcome some of the handicaps of individual techniques, such as collaborative filtering, content-oriented filtering, and knowledge-driven systems. We look at the different methodologies and address challenges in the direction of scalability, sparsity, and cold-start problems. This paper also offers a comparative insight of how hybrid systems improve user engagement compared to the traditional recommender system techniques. Finally, this study address the challenges faced by the hybrid systems and the future scope of research.