Recommendation System

Recommendation systems aim to predict user preferences and provide personalized suggestions, primarily focusing on improving accuracy, diversity, and efficiency. Current research emphasizes incorporating diverse data sources (text, images, location, user interactions across platforms) into sophisticated models, including transformer networks, graph neural networks, and large language models, often within federated learning frameworks to address privacy concerns. These advancements are crucial for enhancing user experience across various applications (e-commerce, social media, search engines) and for developing more robust, explainable, and bias-mitigated systems.