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Volumn 6, Issue 4, 2015, Pages

The netflix recommender system: Algorithms, business value, and innovation

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS;

EID: 84953409830     PISSN: 2158656X     EISSN: 21586578     Source Type: Journal    
DOI: 10.1145/2843948     Document Type: Article
Times cited : (1212)

References (19)
  • 1
    • 84953442588 scopus 로고    scopus 로고
    • Retrieved December 6, 2015 from
    • Chris Alvino and Justin Basilico. 2015. Learning a Personalized Homepage. Retrieved December 6, 2015 from http://techblog.netflix.com/2015/04/learning-personalized-homepage.html.
    • (2015) Learning A Personalized Homepage
    • Alvino, C.1    Basilico, J.2
  • 4
    • 84859070397 scopus 로고    scopus 로고
    • Large-scale validation and analysis of interleaved search evaluation
    • Olivier Chapelle, Thorsten Joachims, Filip Radlinski, and Yisong Yue. 2012. Large-scale validation and analysis of interleaved search evaluation. ACM Transactions on Information Systems 30, 1. DOI:http://dx.doi.org/10.1145/2094072.2094078
    • (2012) ACM Transactions on Information Systems , vol.30 , pp. 1
    • Chapelle, O.1    Joachims, T.2    Radlinski, F.3    Yue, Y.4
  • 5
    • 84874264953 scopus 로고    scopus 로고
    • Improving the sensitivity of online controlled experiments by utilizing pre-experiment data
    • Alex Deng, Ya Xu, Ron Kohavi, and Toby Walker. 2013. Improving the sensitivity of online controlled experiments by utilizing pre-experiment data. In WSDM.
    • (2013) WSDM
    • Deng, A.1    Xu, Y.2    Kohavi, R.3    Walker, T.4
  • 8
    • 85008044987 scopus 로고    scopus 로고
    • Matrix factorization techniques for recommender systems
    • Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 8, 30-37.
    • (2009) Computer , vol.8 , pp. 30-37
    • Koren, Y.1    Bell, R.2    Volinsky, C.3
  • 12
    • 57949113756 scopus 로고    scopus 로고
    • Improving regularized singular value decomposition for collaborative filtering
    • Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD Cup and Workshop. 5-8.
    • (2007) Proceedings of KDD Cup and Workshop , pp. 5-8
    • Paterek, A.1
  • 13
    • 84953442591 scopus 로고    scopus 로고
    • Internet. Retrieved December 6, 2015 from
    • Leo Pekelis, David Walsh, and Ramesh Johari. 2015. The New Stats Engine. Internet. Retrieved December 6, 2015 from http://pages.optimizely.com/rs/optimizely/images/stats-engine-technical-paper.pdf.
    • (2015) The New Stats Engine
    • Pekelis, L.1    Walsh, D.2    Johari, R.3
  • 14
    • 84872255037 scopus 로고    scopus 로고
    • Retrieved December 6, 2015 from
    • Netflix Prize. 2009. The Netflix Prize. Retrieved December 6, 2015 from http://www.netflixprize.com/.
    • (2009) The Netflix Prize
    • Prize, N.1
  • 18
    • 85012223520 scopus 로고    scopus 로고
    • Appendix 2: Metrics and the Statistics behind A/B Testing
    • Dan Siroker and Pete Koomen Eds Wiley, Hoboken, NJ
    • Bryan Gumm. 2013. Appendix 2: Metrics and the Statistics Behind A/B Testing. In A/B Testing: The Most Powerful Way to Turn Clicks into Customers, Dan Siroker and Pete Koomen (Eds.). Wiley, Hoboken, NJ.
    • (2013) A/B Testing: The Most Powerful Way to Turn Clicks into Customers
    • Gumm, B.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.