Personalization at Scale: Big Data Technologies in Digital Content Delivery and Their Effect on Viewer Experience
DOI:
https://doi.org/10.32628/CSEIT241051076Keywords:
Big Data Analytics, Digital Content Delivery, Video Streaming Services, Customer Experience Optimization, Personalized RecommendationsAbstract
This article examines the transformative impact of big data technologies on the digital content delivery industry, focusing on their application in enhancing customer experience through video streaming services. Through a comprehensive case study of a leading provider, we investigate the implementation of Hadoop and AWS Big Data product suites to optimize content delivery, personalize user recommendations, and enable dynamic ad insertion. Our findings demonstrate significant improvements in service reliability, viewer engagement, and advertising effectiveness. The article reveals how Big Data analytics facilitate real-time content optimization and personalized user experiences, increasing viewer retention and satisfaction. Furthermore, we explore the challenges and limitations encountered during implementation, providing insights into the practical applications of Big Data in the evolving landscape of digital media consumption. This article contributes to the growing body of literature on Big Data applications in media and entertainment, offering valuable insights for practitioners and researchers in digital content delivery.
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References
G. Loebbecke and A. Picot, "Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda," The Journal of Strategic Information Systems, vol. 24, no. 3, pp. 149-157, 2015. https://doi.org/10.1016/j.jsis.2015.08.002 DOI: https://doi.org/10.1016/j.jsis.2015.08.002
M. Hashem, I. A. T. Hashem, M. M. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. Ullah Khan, "The rise of "big data" on cloud computing: Review and open research issues," Information Systems, vol. 47, pp. 98-115, 2015. https://doi.org/10.1016/j.is.2014.07.006 DOI: https://doi.org/10.1016/j.is.2014.07.006
C. A. Gomez-Uribe and N. Hunt, "The Netflix recommender system: Algorithms, business value, and innovation," ACM Transactions on Management Information Systems, vol. 6, no. 4, pp. 1-19, 2015. https://doi.org/10.1145/2843948 DOI: https://doi.org/10.1145/2843948
J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston, and D. Sampath, "The YouTube video recommendation system," in Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys '10), pp. 293–296, 2010. https://doi.org/10.1145/1864708.1864770 DOI: https://doi.org/10.1145/1864708.1864770
R. K. Yin, "Case Study Research and Applications: Design and Methods," Sage Publications, 6th edition, 2017. https://www.google.com/books/edition/Case_Study_Research_and_Applications/6DwmDwAAQBAJ
M. Zaharia, R. S. Xin, P. Wendell, T. Das, M. Armbrust, A. Dave, X. Meng, J. Rosen, S. Venkataraman, M. J. Franklin, A. Ghodsi, J. Gonzalez, S. Shenker, and I. Stoica, "Apache Spark: A unified engine for big data processing," Communications of the ACM, vol. 59, no. 11, pp. 56-65, 2016. https://doi.org/10.1145/2934664 DOI: https://doi.org/10.1145/2934664
J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, "Recommender systems survey," Knowledge-Based Systems, vol. 46, pp. 109-132, 2013. https://doi.org/10.1016/j.knosys.2013.03.012 DOI: https://doi.org/10.1016/j.knosys.2013.03.012
M. Luca, "Designing Online Marketplaces: Trust and Reputation Mechanisms," Innovation Policy and the Economy, vol. 17, pp. 77-93, 2017. https://www.journals.uchicago.edu/doi/10.1086/688845 DOI: https://doi.org/10.1086/688845
S. Fan, R. Y. K. Lau, and J. L. Zhao, "Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix," Big Data Research, vol. 2, no. 1, pp. 28-32, 2015. https://doi.org/10.1016/j.bdr.2015.02.006 DOI: https://doi.org/10.1016/j.bdr.2015.02.006
S. Barocas and A. D. Selbst, "Big Data's Disparate Impact," California Law Review, vol. 104, no. 3, pp. 671-732, 2016. https://www.jstor.org/stable/24758720
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