| Abstract: |
Recommendation systems have become an integral component of modern digital platforms, enabling personalized content delivery across e-commerce, streaming services, and social media. Traditional collaborative filtering techniques, while effective, suffer from well-documented limitations including data sparsely, cold-start problems, and scalability constraints. Concurrently, deep learning methods have demonstrated remarkable capability in extracting latent feature representations from complex, high-dimensional data. This paper presents a comprehensive review and meta-analysis of hybrid recommendation approaches that integrate deep learning with collaborative filtering to overcome the inherent weaknesses of each paradigm when applied in isolation [1]. Through a systematic survey of 85 peer-reviewed publications published between 2015 and 2023, this review examines the evolution of hybrid architectures, fusion strategies, and performance evaluation methodologies. The meta-analysis reveals that hybrid deep learning-collaborative filtering systems consistently outperform standalone approaches, achieving mean improvements of 7.2% in RMSE and 11.5% in precision metrics across benchmark datasets [2]. The review further identifies key architectural patterns, including attention-based fusion mechanisms, graph neural network augmentations, and multi-task learning frameworks that represent the current state of the art. Critical gaps in reproducibility, dataset standardization, and real-world deployment evaluation are identified, and corresponding future research directions are proposed to advance the field toward more robust, scalable, and interpretable hybrid recommendation systems. |
