Optimized Advancements in Next-Generation Recommendation Engines for Personalized Experiences on OTT Platforms

The rapid growth of Over-The-Top (OTT) platforms has led to an explosion of available content, presenting a significant challenge for users to discover content tailored to their preferences. To address this issue, recommendation engines powered by artificial intelligence (AI) have emerged as crucial tools for enhancing content discovery experiences. This research paper delves into the landscape of recommendation engines on OTT platforms, exploring their underlying algorithms, methodologies, and impact on user engagement. The paper begins by providing an overview of the escalating demand for personalized content consumption and the role of recommendation systems in fulfilling this demand. It examines various collaborative filtering, content-based filtering, and hybrid recommendation techniques employed in the context of OTT platforms, highlighting their strengths and limitations.