Application of Recommendation System Technology and Architecture in Video Streaming Platforms

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Abstract
With the rapid development of video streaming platforms and the diversification of user content consumption habits, personalized recommendation systems have gradually become a critical technology for enhancing user experience, increasing user engagement, and optimizing platform profitability. This paper systematically studies the technical architecture and application scenarios of recommendation systems in video streaming platforms, focusing on the unique challenges in handling large volumes of video data, analyzing user behavior, and delivering personalized content recommendations. By examining the overall architecture of recommendation systems, data processing, feature engineering, model design, and optimization strategies, this paper summarizes the application effects of various commonly used recommendation algorithms on video streaming platforms and explores the value of recommendation systems in these platforms through practical case studies. Additionally, this paper discusses the future challenges and potential technological breakthroughs in recommendation systems, such as deep learning and multi-modal feature fusion, reinforcement learning and real-time recommendations, as well as privacy protection and fairness research. The research findings indicate that a well-designed and optimized recommendation system can effectively enhance the depth of user content exploration and the overall activity level of the platform, providing strong support for the sustainable development of video streaming platforms.