Artificial Intelligence (AI)–driven recommendation systems play a pivotal role in shaping user experiences on social commerce platforms by personalizing content, products, and interactions. By analyzing vast amounts of user-generated data—such as browsing behavior, social connections, preferences, and engagement patterns—AI • recommendations help platforms deliver highly relevant product suggestions in real time. This personalization enhances user satisfaction, reduces information overload, and increases trust in platform-provided recommendations, thereby fostering deeper engagement and more informed purchasing decisions. • Moreover, AI-powered recommendations significantly influence commercial outcomes by driving conversion rates, impulse buying, and customer loyalty within social commerce ecosystems. Through techniques such as collaborative filtering, deep learning, and sentiment analysis, these systems adapt dynamically to changing user preferences and social trends. However, challenges related to data privacy, algorithmic bias, and transparency remain critical concerns. Addressing these issues is essential to ensuring ethical, trustworthy, and sustainable use of AI recommendations in social commerce platforms, while maximizing their potential to create value for both consumers and businesses.
Mr. Prakash Devidas Shinde (Sun,) studied this question.