AI-driven Content Recommendation Engine for Live Broadcasting

As a startup in the broadcasting industry, we aim to revolutionize the viewer experience by developing an AI-driven content recommendation engine. The project will focus on utilizing cutting-edge technologies such as Natural Language Processing (NLP), Computer Vision, and Predictive Analytics to analyze viewer interactions and content metadata in real-time. The goal is to offer personalized content recommendations during live broadcasts, thereby increasing viewer engagement and retention. Leveraging OpenAI's API and advanced frameworks such as TensorFlow and PyTorch, the system will process large volumes of data efficiently. By integrating with live streaming platforms, the engine will dynamically adjust recommendations based on real-time viewer behavior and preferences. This project seeks a balance between accuracy and computational efficiency, deploying models using AutoML and Edge AI for swift processing and rapid deployment. The project demands a quick turnaround, with a development timeline of 4-6 weeks. Given the competitive landscape, having a sophisticated recommendation system is crucial for differentiating our platform, enhancing user experience, and driving growth.