Hybrid AI Models for Dynamic Content Recommendation in Streaming Services

Abstract

With the rise of streaming services such as Netflix, Amazon Prime Video, and Spotify, content recommendation systems have become a critical component of user engagement and retention. Traditional recommendation techniques like collaborative filtering and content-based filtering have their limitations, particularly when dealing with the cold start problem or insufficient data. This paper explores the application of hybrid AI models to overcome these challenges and provide dynamic, personalized content recommendations. By combining deep learning, reinforcement learning, and multi-modal data processing, hybrid AI models offer a robust framework for understanding user preferences and adapting to real-time feedback. The proposed system is capable of continuously learning and adjusting its recommendations based on user behavior, content metadata, and contextual data, such as time and device type. Experimental results demonstrate that hybrid AI models significantly enhance recommendation accuracy, content popularity prediction, and user engagement, making them a promising solution for next-generation streaming platforms.