AI-Powered Content Recommendation Engine for Streaming Platforms

As a scale-up company in the streaming platforms industry, we are looking to enhance our user experience by developing a cutting-edge content recommendation engine. The goal is to utilize advanced AI and machine learning techniques, including large language models (LLMs), computer vision, and predictive analytics, to create a system that offers personalized content recommendations to our users. The project involves integrating technologies such as OpenAI API, TensorFlow, PyTorch, Langchain, Pinecone, and Hugging Face to develop a scalable and efficient recommendation engine. Key functionalities include analyzing user viewing habits, content preferences, and behavioral patterns to predict and suggest content likely to engage viewers. The engine will also use computer vision to categorize and tag vast libraries of content, enabling nuanced content discovery. This initiative not only aims to increase user engagement and retention but also provide a competitive edge in a saturated market. The project is expected to span 8-12 weeks, with a budget allocation of $15,000 to $50,000, and holds a medium to high urgency due to upcoming strategic launches and competitive pressures.