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Abstract
The rapid growth of big data has created new opportunities for developing intelligent systems capable of supporting effective decision-making processes. In this context, recommendation systems play a crucial role by providing personalized and data-driven suggestions to users. However, traditional recommendation approaches often face limitations in scalability, accuracy, and the ability to process large-scale heterogeneous data. This paper focuses on the design of an intelligent recommendation system based on big data analytics. The study analyzes modern big data technologies and machine learning methods used for collecting, processing, and analyzing large volumes of structured and unstructured data. A system architecture is proposed that integrates data preprocessing techniques, big data platforms, and intelligent algorithms to improve recommendation quality.
Experimental results demonstrate that the proposed approach enhances recommendation accuracy and system performance compared to conventional methods. The findings highlight the effectiveness of big data–driven analytics in developing scalable and intelligent recommendation systems. The results of this study can be applied in various domains such as e-commerce, education, and decision support systems, and provide a foundation for future research in intelligent data-driven system design.
References
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