Optimizing video click-through rates with bandit algorithms

Abstract

In recent years, videos have increasingly influenced public perception, making video platforms a focal point of digital consumption. One critical challenge for platform operators is identifying videos that resonate most with users, as user ratings directly reflect viewer preferences and experiences. This study explores the use of bandit algorithms to predict and strategize the overall ratings of various anime videos on the Bilibili platform. Bandit algorithms, a subset of the multi-armed bandit model, dynamically adjust selection strategies based on prior feedback to maximize cumulative rewards. Our empirical research assessed multiple gambling algorithms, including the -greedy, Upper Confidence Bound (UCB), Explore-then-Commit (ETC), and Thompson Sampling (TS) algorithms. The findings indicate that the Thompson Sampling algorithm, in particular, achieved the lowest cumulative regret in selecting optimal videos on the Bilibili platform, showcasing its superior performance. This study highlights the potential of bandit algorithms to enhance video selection processes, ensuring that platforms can effectively cater to user preferences and enhance viewer satisfaction.