MULTIMEDIA RECOMMENDATION SYSTEM

The exponential growth of digital content across disparate domains—including video streams, music libraries, and textual documents—has exacerbated the challenge of information overload, critically hindering personalized content discovery. While domain-specific recommendation systems (RS) are highly optimized, they often fail to effectively bridge the structural and semantic gaps present in a heterogeneous multimedia environment. This project addresses this limitation by designing, implementing, and rigorously evaluating a robust Multimedia Recommendation System (MRS) prototype. The core objective is the development of a novel hybrid recommendation architecture capable of intelligently fusing diverse data streams: low-level visual features (extracted via Convolutional Neural Networks), high-level textual metadata (processed using natural language embeddings), and explicit/implicit collaborative filtering signals. This integrated framework utilizes Deep Neural Networks (DNNs) to learn shared, latent representations across modalities, allowing for coherent cross-media prediction and mitigating the critical cold-start problem. The methodology is threefold: (1) Developing comprehensive data pipelines for high-dimensional feature extraction across images, video, and text; (2) Implementing a custom fusion layer optimized for weighting the contribution of each modality; and (3) Deploying a functional prototype tested on a large public multimedia dataset. The system's performance is quantitatively assessed against state-of-the-art baseline models using standard metrics, including Precision@K, Recall, and Mean Average Precision (MAP). The results demonstrate the enhanced efficacy and relevance of the integrated MRS in providing coherent, novel, and highly personalized cross-media recommendations, thereby validating a flexible and scalable solution for superior content delivery in complex digital ecosystems.