Enhancing Personalized Recommendation Systems: A Hybrid Etc-Ucb Bandit Algorithm Approach

In the era of information overload, personalized recommendation systems have become indispensable tools for enhancing user experience by filtering relevant content. However, the exploration-exploitation tradeoff remains a critical challenge, where systems must balance between leveraging known user preferences (exploitation) and exploring potential new interests (exploration). This paper introduces a hybrid Equal-Time Exploration followed by an Upper Confidence Bound (ETC-UCB) bandit algorithm to address this challenge. The proposed algorithm divides the recommendation process into two phases: an initial Equal-Time Exploration (ETC) phase to ensure uniform exploration of all content categories and a subsequent Upper Confidence Bound (UCB) phase to exploit historical data for optimal arm selection. Empirical evaluations on the MovieLens dataset demonstrate that the ETC-UCB algorithm significantly reduces cumulative regret across varying interaction scales (n = 50, 500, 5000, 50000), with regret growth adhering to theoretical logarithmic bounds and stable standard deviations indicating robust performance. By integrating structured exploration with adaptive exploitation, this study provides a novel framework for real-time optimization in dynamic recommendation environments, bridging the gap between theoretical bandit models and practical system implementations.