Enhancing User Engagement with AI-driven Content Recommendations for Streaming Platforms

In the competitive landscape of streaming platforms, user engagement and retention are critical metrics for success. Our scale-up company has identified a key opportunity to improve these metrics by implementing an AI-driven content recommendation system. The project will involve utilizing large language models (LLMs) and computer vision technologies to analyze user behavior and content metadata, creating highly personalized recommendations. By integrating TensorFlow and PyTorch, we aim to develop a robust machine learning model capable of processing vast amounts of data in real-time. We will use Natural Language Processing (NLP) and predictive analytics to understand user feedback and predict emerging trends. The solution will also incorporate AutoML for continuous optimization and Edge AI to ensure efficient processing with minimal latency. By the end of this project, we expect to significantly boost our user engagement rates, reduce churn, and position ourselves as a leader in personalized streaming experiences.