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Scientific Literature
Scientific Literature
In this page, we provide an outline of the six scientific papers used for the Case File analysis. It consists of four sections that cover the different aspects of Netflix's online recommendations: recommender systems, personalized recommendations, collaborative filtering algorithms, and Netflix's recent improvements. Each section contains a summary of the literature, which is referenced and listed below.
Amatriain, X., & Basilico, J. (2016, September). Past, Present, and Future of Recommender Systems: An Industry Perspective. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 211-214). ACM.
2 citations
Amatriain, X. (2013). Big & Personal: data and models behind Netflix recommendations. Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining Algorithms, Systems, Programming Models and Applications - BigMine '13. doi:10.1145/2501221.2501222
35 citations
Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (n.d.). Collaborative Filtering Recommender Systems. The Adaptive Web Lecture Notes in Computer Science, 291-324. doi:10.1007/978-3-540-72079-9_9
1144 citations
Koren, Y., Bell, R., & Volinsky, C. (2009) Matrix Factorization Techniques for Recommender Systems. Computer, 42(8), 30–37. doi:10.1109/mc.2009.263
3262 citations
Salakhutdinov, R., Mnih, A. & Hinton, G. (2007). Restricted Boltzmann Machines for Collaborative Filtering. Proceedings of the 24th International Conference on Machine Learning, Corvallis, OR.
822 citations
Gomez-Uribe, C. A., & Hunt, N. (2016). The Netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 13.
61 citations
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