ENHANCING CUSTOMER EXPERIENCE THROUGH ML-BASED RECOMMENDATION SYSTEMS

Abstract

In the digital age, delivering personalized and seamless customer experiences has become a strategic imperative for businesses. Machine Learning (ML)-based recommendation systems have emerged as powerful tools to meet this demand by analyzing user behavior, preferences, and historical data to generate relevant suggestions. These systems not only enhance user satisfaction and engagement but also drive sales and brand loyalty. This paper explores the methodologies, algorithms, and real-world applications of ML-powered recommendation engines across various industries. It also discusses the challenges related to data privacy, algorithmic bias, and scalability, while highlighting future directions for creating more adaptive and ethical recommendation systems that prioritize customer-centric innovation.