Recommender Systems in Industry: A Netflix Case Study

Author:

Amatriain Xavier,Basilico Justin

Publisher

Springer US

Reference98 articles.

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2. Agarwal, D., Chen, B.C., Pang, B.: Personalized recommendation of user comments via factor models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP ‘11, pp. 571–582. Association for Computational Linguistics, Stroudsburg, PA, USA (2011). URL http://dl.acm.org/citation.cfm?id=2145432.2145499

3. Ahmed, A., Teo, C.H., Vishwanathan, S., Smola, A.: Fair and balanced: Learning to present news stories. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM ‘12, pp. 333–342. ACM, New York, NY, USA (2012). DOI 10.1145/2124295.2124337. URL http://doi.acm.org/10.1145/2124295.2124337

4. Amatriain, X., Lathia, N., Pujol, J.M., Kwak, H., Oliver, N.: The wisdom of the few: a collaborative filtering approach based on expert opinions from the web. In: Proc. of 32nd ACM SIGIR, SIGIR ‘09, pp. 532–539. ACM, New York, NY, USA (2009). DOI 10.1145/1571941.1572033. URL http://dx.doi.org/10.1145/1571941.1572033

5. Amatriain, X., Pujol, J.M., Oliver, N.: I Like It…I Like It Not: Evaluating User Ratings Noise in Recommender Systems. In: G.J. Houben, G. McCalla, F. Pianesi, M. Zancanaro (eds.) User Modeling, Adaptation, and Personalization, vol. 5535, chap. 24, pp. 247–258. Springer Berlin (2009). DOI 10.1007/978-3-642-02247-0_24. URL http://dx.doi.org/10.1007/978-3-642-02247-0_24

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