Optimizing Short Video Recommendation Systems: Addressing Cold-Start and Diversity Challenges through Advanced Algorithms

This study reviews the integration of advanced algorithms in optimizing short video recommendation systems, specifically addressing the cold-start problem and content diversity. Traditional recommendation algorithms struggle to adapt to dynamic user interests and often fail in scenarios with limited historical data. To tackle these issues, we proposed two novel approaches by integrating the Portrait Upper Confidence Bound (PUCB) algorithm and the Exp3 algorithm. The PUCB algorithm effectively balances exploration and exploitation in cold-start scenarios by leveraging user portraits and UCB scores, while Exp3 enhances content diversity by dynamically adjusting recommendation probabilities. And the combined application of PUCB and Exp3 can greatly enhance the performance of short video platforms, providing more relevant and varied content to users.