Junhai Zeng

A Comparative Study of Multi-Armed Bandit Algorithms for Dynamic Filter and Effect Recommendations on Tiktok

2025 · ITM Web of Conferences

This study assesses how three benchmark Multi-Armed Bandit (MAB) methods—ε-greedy, Upper Confidence Bound (UCB), and Thompson sampling—perform in dynamically recommending filters and effects for short-video platforms. An experiment was configured using real user engagement metrics …