A Review of Machine Learning-Based Recommendation Algorithms in Information Technology Systems
DOI:
https://doi.org/10.71222/gvtd3173Keywords:
recommendation systems, machine learning, deep learning, content-based filtering, collaborative filtering, hybrid models, data sparsity, fairness, transparency, quantum computingAbstract
This review explores the evolving landscape of recommendation systems, focusing on the integration of machine learning techniques to enhance personalization and effectiveness. It high-lights various approaches, including content-based filtering, collaborative filtering, and hybrid models, while examining the role of advanced machine learning methods such as deep learning and reinforcement learning. The discussion addresses the challenges faced by current algorithms, including data sparsity, scalability issues, and biases. Future directions for research are proposed, emphasizing the integration of emerging technologies like quantum computing, the enhancement of fairness and transparency, and the development of real-time adaptive systems. This re-view aims to provide insights into the current state of recommendation systems and their potential advancements, contributing to more effective and user-centric applications across diverse domains.
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