The evolution and impact of Multi-Armed Bandit algorithms in social media

Abstract

This paper examines the transformative impact of Multi-Armed Bandit (MAB) algorithms on user experiences across social media platforms. Initially conceptualized in the 1930s and formalized in the 1950s, MAB algorithms have become foundational to the evolution of digital interactions and content personalization. These algorithms adeptly navigate the trade-off between exploration and exploitation to maximize user engagement and satisfaction. By scrutinizing their implementation from early adopters like Yahoo to contemporary giants such as Facebook, Instagram, and TikTok, this analysis elucidates the algorithms' prowess in tailoring content recommendations, refining advertising strategies, and bolstering overall platform engagement. Moreover, this study addresses the ethical dimensions of MAB algorithms, with a particular emphasis on concerns surrounding user privacy and the perpetuation of echo chambers. Through an extensive synthesis of theoretical insights and empirical applications, this paper highlights the pivotal role of MAB algorithms in shaping the digital and social media landscape, advocating for future research focused on improving algorithmic transparency and ethical governance.