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Volume 8, Issue 1

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Volume 8, Issue 1

ANTECEDENTS OF RECOMMENDATION FRAMEWORK ENABLED THROUGH ARTIFICIAL INTELLIGENCE ON NETFLIX PLATFORM

Author(s)

Aman Tibrewal, Rupashi Sehgal, Dr. Shikha Singh

Affiliations

Symbiosis Centre for Management Studies, NOIDA, Symbiosis International (Deemed University), Pune India

Abstract

With the rising technological advancements, various tools like Artificial Intelligence and recommendation are utilized by companies to gain an upper edge in the markets. This paper aims to gain a deeper understanding of how Netflix uses Artificial Intelligence to enable effective usage of the recommendation engines in their business model. To conduct an effective study, this paper follows a triangulation method, under which a literature review was conducted initially to gain a conceptual understanding of the topic. Based on it, structured personal interviews and a survey was conducted. Lastly, a quantitative analysis was executed to gain a deeper insight into the data as collected. This paper aims to gain a deeper insight into the functioning of recommendation systems for Netflix’s Over-the-top (OTT) media services. The paper also focuses upon the usage of Artificial Intelligence technology which acts as a key enabler for Recommendation Engines for efficient and effective utilization to gain a competitive advantage for Netflix. This paper is of value to each individual who seeks to understand the mind behind the model of Netflix and how the user interface allows them to collect data and research upon the viewing habits of their users. It moreover also focuses on the integration of Artificial intelligence in the Recommendation engine at Netflix.

Keywords

Recommendation Engine, Netflix, Over- the-top (OTT) Media Service, Artificial Intelligence

References
  1. Ansari, A., Essegaier, S., & Kohli, R. (2000). Internet Recommendation Systems. Journal of Marketing Research, 37(3), 363–375. doi:https://doi.org/10.1509%2Fjmkr.37.3.363.18 779.
  2. Bartlett, M. S. (1951). The effect of standardization on a χ 2 approximation in factor analysis. Biometrika, 38 (3/4), 337–344.Brown, T. A. (2015). Confirmatory factor analysis for applied research. New York: The Guilford Press.
  3. Codecademy. (2020). Netflix Recommendation Engine. Retrieved from https://www.codecademy.com/articles/how- netflix-recommendation-works-data-science
  4. DeVellis, R. F. (2012). Scale development: Theory and applications (3rd ed). California: Sage publications
  5. Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 1-19.
  6. Gorsuch, R. L. (2014). Exploratory factor analysis. New York: Routledge.
  7. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. New Jersey: Prentice Hall.
  8. Haton, J. P. (2006). A brief introduction to artificial intelligence. IFAC Proceedings Volumes, 39(4), 8-16.
  9. Havens, T. (2018). Netflix: Streaming channel brands as global meaning systems. In from Networks to Netflix (pp. 321-331). Routledge.
  10. Heale, R., & Forbes, D. (2013). Understanding triangulation in research. Evidence-based nursing, 16(4), 98-98.
  11. Kaiser, H. F., & Rice, J. (1974). Little jiffy, mark iv. Educational and Psychological Measurement, 34 (1), 111–117.
  12. Kotu, V., & Deshpande, B. (2019). Recommendation engines. Data Science (Second Edition), 343-394
  13. Liftigniter. (2016, March 15). The Power of Netflix’s Personalized Video Recommendations. Retrieved from https://www.liftigniter.com/netflix- personalizedvideo
  14. Maxxor. (2017). Netflix’s Cultural Influence on Global Culture. (https://www.maxxor.com/) Retrieved from https://www.maxxor.com/blog/netflix-cultural- influence-global-culture/
  15. Pilipets, E. (2019). From Netflix Streaming to Netflix and Chill: The (Dis) Connected Body of Serial Binge-Viewer. Social Media + Society, 5(4), 2056305119883426.
  16. Rose, J. A. (1988). Expert Systems by A. Bonnet, JP Haton, JM Truong-Ngoc Prentice Hall, New York, 1988 (£ 11.95). Robotica, 6(4), 343-344.
  17. Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5), 513-523
  18. Shapiro, S. C. (1992). Encyclopedia of artificial intelligence second edition. John.
  19. Thurmond, V. A. (2001). The point of triangulation. Journal of nursing scholarship, 33(3), 253-258.
  20. Victor. (2007, February 14). Netflix’s Competitive Advantage. (Victor, Ed.) Internet, Organizations, 1(1). Retrieved from http://noisebetweenstations.com/personal/weblog s/?p=2011
  21. Vom Brocke, J. and Sinnl, T. (2011), "Culture in business process management: a literature review", Business Process Management Journal, 17 (2)357-378. https://doi.org/10.1108/14637151111122383
  22. Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS quarterly, xiii-xxiii.
  23. Wildemuth, B. M. (Ed.). (2016). Applications of social research methods to questions in information and library science. ABC-CLIO.
  24. Yan, J. (2017). Identifying Online Streaming User Value in the Netflix Recommendation System. Drexel University.

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