<![CDATA[The exponential growth of e-commerce data has created an urgent need for efficient and scalable systems that provide personalized product recommendations. This study addresses that challenge by integrating big data processing with neural networks and delivering recommendations via RESTful APIs. The primary objective is to develop a system capable of handling large datasets and providing real-time recommendations to enhance user engagement. The methodology involves using Apache Spark for distributed big data processing and feature engineering, followed by the implementation of neural networks in Python using TensorFlow to generate recommendations. The system integrates the model with a RESTful API to support seamless interaction with external applications. Extensive testing was conducted on a dataset containing over a million user-item interactions to evaluate performance and scalability. The results show that the proposed system achieves better recommendation accuracy compared to traditional machine learning approaches. It processes high-dimensional data efficiently and maintains latency below 200 milliseconds per API request, making it suitable for real-time applications. The novelty of this research lies in the end-to-end design that combines a big data framework with neural networks and RESTful APIs for practical implementation. This research provides a scalable and adaptive solution for e-commerce platforms and serves as a foundation for the advancement of real-time recommendation systems in the future.]]>
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