Big Data-based recommendation systems

Barata, Luís; Mata, Diogo; Nunes, Jorge; Rodrigues, Lucas

http://hdl.handle.net/10400.11/10401

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Introduction: With the exponential growth of data, digital platforms increasingly rely on Big Data technologies to personalize user experiences and improve the accuracy of item recommendations. Recommendation systems play a critical role in e-commerce, entertainment, and social media by analyzing user interactions, behaviors, and preferences. However, the complexity of large-scale data processing and the diversity of filtering techniques pose significant challenges to achieving high performance and scalability. Objectives: This study aims to analyze how Big Data technologies are applied in modern recommendation systems, emphasizing their role in enhancing personalization and system performance. The work seeks to identify the main data collection strategies, algorithms, and tools adopted in recent research, as well as to assess how these systems address challenges related to scalability and real-time processing. Methods: A systematic literature review was conducted using the IEEE Xplore database, focusing on articles published between 2015 and 2025. The search targeted studies combining Big Data and recommendation systems within e-commerce contexts. Out of 49 retrieved publications, 10 met the inclusion criteria, and 5 were ultimately selected after applying exclusion filters. Each selected paper was analyzed regarding its objectives, employed algorithms, data sources, and achieved outcomes. Results: The reviewed studies demonstrate a wide variety of approaches and technologies, including Hadoop and Spark frameworks for large-scale data processing, deep learning models such as DeepLearning4j for real-time prediction, and classical data mining algorithms like K-Means, Apriori, and FP-Growth. Hybrid methods combining collaborative and content-based filtering were shown to overcome limitations such as the cold start and data sparsity problems. Scalability was addressed through distributed processing and optimization techniques like network pruning and parallel computation. These systems achieved higher recommendation precision and responsiveness across different e-commerce and media applications. Conclusions: The analysis confirms that Big Data–driven recommendation systems are essential for enhancing user engagement and conversion in digital platforms. By integrating data mining, machine learning, and distributed processing technologies, these systems deliver efficient, adaptive, and context-aware recommendations. Future developments should continue exploring hybrid and deep learning–based approaches to further improve scalability, personalization, and computational performance in increasingly complex digital ecosystems.