Intelligent algorithm research on content generation and personalized recommendation for short video live streaming platforms

With the explosive growth of the total amount of global information, the problem of information overload can-not be avoided and is becoming more and more serious. Personalized recommendation system is an effective technology to solve the problem of information overload, and short video live broadcasting platform has been rapidly rising in the Internet field in recent years, becoming an important channel for users to obtain information and entertainment. Content generation and personalized recommendation technology, as a key link in the core competitiveness of the platform, plays an important role in the enhancement of user experience and the realization of platform value. However, existing algorithms still have many challenges in terms of diversity, real-time and fairness. In this paper, we systematically analyze the core technology and optimization method of the relevant algorithms for the content generation and personalized recommendation problem of short video live broadcasting platform. It explores the construction and practice of deep learning-based content generation model; studies the optimization strategy of user behavior data-driven personalized recommendation algorithm, including the construction of user profile, hybrid recommendation technology and algorithmic fairness improvement method; and analyzes the potential challenges and development direction of algorithms in privacy protection, ethical considerations and multimodal interactions in combination with the platform application scenarios. This study aims to provide theoretical support and technical reference for the intelligent development of short video platforms.