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In the digital realm, recommendation systems are pivotal in shaping user experiences on online platforms, tailoring content based on user feedback. A notable algorithm in this domain is the multi-armed bandit algorithm, with the Upper Confidence Bound (UCB) emerging as a classic and effective variant. This paper delves into an array of Upper Confidence Bound algorithm variations, encompassing UCB1, Asymptotically Optimal UCB, UCB-V, and UCB1Tuned. The research harnesses the MovieLens dataset to assess the performance of these algorithms, employing cumulative regret as the primary metric. For l in UCB1 and c in UCB-V, both oversized and undersized parameters will result in negative outcomes. And UCB1Tuned outperforms the other three algorithms in this experiment, since it considers variance and adjusts parameters dynamically. The study demonstrates that setting a appropriate UCB index is crucial for enhancing the performance of the UCB algorithm in recommendation system. It holds significance for both improve recommendation system algorithms and enhance user experience.
Qi He (Wed,) studied this question.