Abstract
The rapid growth of streaming platforms has transformed the media consumption landscape, providing unique opportunities for advertisers to reach highly targeted audiences. Personalized advertising has emerged as a powerful tool to enhance user engagement by delivering relevant content tailored to individual preferences and behaviors. This paper explores the various techniques employed to optimize personalized ads, focusing on the role of data analytics, machine learning, and artificial intelligence in understanding user preferences and consumption patterns. By leveraging real-time data, advertisers can create dynamic, adaptive ads that resonate more deeply with users, leading to improved engagement and retention rates. Additionally, the study examines the ethical implications of data privacy and user trust, highlighting the balance between personalization and user consent. The findings suggest that strategic implementation of personalized ads, combined with a transparent approach to data usage, can significantly enhance user satisfaction and the overall effectiveness of advertising campaigns on streaming platforms. This abstract introduces the key techniques and challenges in personalizing ads for streaming platforms, emphasizing user engagement and ethical considerations.
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