Leveraging AI for Digital User Behaviour Prediction and Recommendation System: A Comprehensive Study

Abstract

To enhance personalization and precision in digital ecosystems, this study applies AI to prediction and recommendation systems for digital user behavior. The purpose of this study is to assess the strengths, weaknesses, and potential applications of various recommendation algorithms, including those that rely on collaboration, content, knowledge, and hybrid models, in order to forecast user preferences. The study focuses on the ways AI enhances the scalability, accuracy of predictions, and flexibility of recommendation systems. Major challenges for recommendation systems driven by AI include scalability, interpretability of models, integration of data from many channels, and issues with privacy and fairness. The review exhibits the capabilities of AI in recommendation schemes and provides strategies for user involvement; nevertheless, it also highlights areas that require more investigation to tackle ethical and technological challenges.