Enhancing User Engagement through AI-Powered Predictive Content Recommendations Using Collaborative Filtering and Deep Learning Algorithms

Abstract

This research paper explores the enhancement of user engagement on digital platforms through AI-powered predictive content recommendations, employing a combination of collaborative filtering and deep learning algorithms. The study addresses the challenges faced by traditional recommendation systems which often suffer from sparsity and scalability issues. By integrating collaborative filtering techniques with deep learning models, such as neural collaborative filtering and recurrent neural networks, the proposed system dynamically predicts user preferences, offering personalized content suggestions. The methodology involves the development of a hybrid model that leverages user-item interaction data and contextual information to enhance recommendation accuracy and diversity. Extensive experiments conducted on publicly available datasets demonstrate the effectiveness of the proposed approach, showing significant improvements in precision, recall, and user satisfaction metrics compared to conventional recommendation systems. The results indicate that the deep learning-based model not only captures complex user-item relationships but also adapts to evolving user interests over time, thereby increasing user engagement. Furthermore, the paper discusses the implications of integrating AI-driven recommendations in various industries, highlighting ethical considerations and potential future advancements. This study provides a comprehensive framework for developing robust recommendation systems that can be applied across diverse digital environments, fostering enhanced user interaction and satisfaction.