Authors: Professor Mayuri Dongre, Aniket Manoj Singh, Deepak Albankar
Abstract: With the exponential growth of user-generated con-tent on social media platforms, recommendation systems have become the primary mechanism for content curation, user engagement, and personalized information delivery. Traditional recommendation approaches, such as collaborative filtering and content-based filtering, increasingly struggle with inherent limi-tations, including data sparsity, cold-start issues, and the highly dynamic, multimodal nature of modern social media networks. This paper provides a comprehensive analysis of contemporary optimization techniques designed to enhance the precision, scala-bility, and diversity of social media recommendation engines. We systematically review the integration of deep learning architec-tures, Graph Neural Networks (GNNs) for structural relationship mapping, and advanced embedding strategies. Furthermore, we investigate critical operational challenges, including algorithmic bias, real-time computational latency, and data privacy regula-tions. Finally, this study outlines pivotal future research direc-tions, highlighting the paradigm shift toward Large Language Model (LLM) integration and autonomous agentic workflows to build next-generation, context-aware, and explainable recommen-dation frameworks.
