Scalable Context-Aware Recommendation System Leveraging Hadoop Ecosystem for Big Data Analytics

hese days, the volume of data is increasing at a faster rate, necessitating scalable, personalized, and effective recommendation systems that can also adapt to changing user context. The impact of contextual elements, such as location, time, and device kinds, is sometimes overlooked by classical recommendation systems, which primarily concentrate on heuristic data and user preferences. This paper proposes a scalable context-aware recommendation framework that leverages the Hadoop ecosystem in order to process and examine big data efficiently. The proposed framework provides more accurate and tailored recommendations across a variety of disciplines by integrating the mentioned described contextual information into the recommendation process. However, the Hadoop ecosystem, which consists of elements like MapReduce, Mahout, Hive, and Hadoop Distributed File Systems (HDFS), is used to handle massive databases that allow for high scalability and performance under heavy data loads. This framework is demonstrated to increase recommendation accuracy by up to 20% when compared to traditional methods through simulations, particularly the association of real-world problem-based databases. As a result, when scaling to databases with more than 10 million records, the processing time ratio is reduced by 30%. Furthermore, the computational efficiency of the suggested framework is demonstrated by the fact that it can process up to 2 Terabytes (TB) of data in less than 7200s. Because of this, the suggested solution can be used in e-commerce, healthcare, and entertainment, and it primarily has to provide real-time, context-sensitive recommendations. This is one of the instances that shows how big data analytics may enhance user experiences by providing recommendations that are both computationally scalable and contextually relevant.