Browsing by Author "Nunes, Jorge"
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- Big Data-based recommendation systemsPublication . Barata, Luís; Mata, Diogo; Nunes, Jorge; Rodrigues, LucasIntroduction: 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.
- Digital platforms to promote sustainable and authentic tourism in low-density territories of Southern Europe: Challenges and opportunitiesPublication . Nunes, Jorge; Mata, Diogo; Ribeiro, Fernando Reinaldo; Metrôlho, José CarlosTourism in low-density regions has gained increasing attention as travelers seek more sustainable and authentic tourism experiences. However, despite their cultural and environmental richness, these territories often face structural challenges such as limited visibility, fragmented promotion, and inadequate digital infrastructure. This study explores how digital platforms can support sustainable tourism development in such contexts, combining a systematic literature review with an exploratory analysis of commercial applications. The analysis focuses on academic initiatives that propose IT-based solutions for promoting tourism in sparsely populated areas of Southern Europe, while the platform analysis assesses functionalities and limitations of widely used applications. The findings reveal that most academic solutions remain at the prototype stage or have yet to be tested in real-world contexts, with limited evidence of large-scale implementation or practical validation. Accessibility for people with functional limitations is also largely neglected in both academic and commercial platforms, despite its importance for inclusive tourism. In addition, the digital landscape remains fragmented, with few solutions effectively designed to bring together diverse local stakeholders or to meaningfully enable user-generated content. The study concludes by identifying key challenges, such as fragmentation, lack of accessibility features, and limited deployment, and outlines future directions for developing scalable, inclusive, and culturally sensitive platforms tailored to the realities of low-density territories.