Enhancing Social Media Experiences Through Recommendation Systems: Techniques, Applications, And Future Prospects

Authors: Onkar R. Malawade

Abstract: Recommendation systems play a pivotal role in transforming user engagement on social media platforms by curating personalized content feeds, friend suggestions, advertisements, and more. This paper presents a comprehensive review of recommendation techniques applied in social media, including collaborative filtering, content-based filtering, graph-based models, and transformer-powered algorithms. It explores how these systems enhance user satisfaction, interaction, and platform retention. Drawing on over 180 peer-reviewed studies and industry implementations, this paper examines the technological foundations of social media recommenders, with a special focus on trends like deep learning, user profiling, and real-time adaptation. Critical issues such as filter bubbles, algorithmic bias, privacy concerns, and limited interpretability are also discussed. Finally, the paper suggests pathways for ethical, explainable, and inclusive social recommendation systems.