Editing Recommender system (section)

== The Netflix Prize ==
{{main|Netflix Prize}}

One of the events that energized research in recommender systems was the [[Netflix Prize]]. From 2006 to 2009, Netflix sponsored a competition, offering a grand prize of $1,000,000 to the team that could take an offered dataset of over 100 million movie ratings and return recommendations that were 10% more accurate than those offered by the company's existing recommender system. This competition energized the search for new and more accurate algorithms. On 21 September 2009, the grand prize of US$1,000,000 was given to the BellKor's Pragmatic Chaos team using tiebreaking rules.<ref name="nytimes.com">{{cite news|last1=Lohr|first1=Steve|title=A $1 Million Research Bargain for Netflix, and Maybe a Model for Others|url=https://www.nytimes.com/2009/09/22/technology/internet/22netflix.html|website=The New York Times|date=22 September 2009 }}</ref>

The most accurate algorithm in 2007 used an ensemble method of 107 different algorithmic approaches, blended into a single prediction. As stated by the winners, Bell et al.:<ref>{{cite web |url = http://www.netflixprize.com/assets/ProgressPrize2007_KorBell.pdf |author1 = R. Bell |author2 = Y. Koren |author3 = C. Volinsky |title = The BellKor solution to the Netflix Prize |year = 2007 |access-date = 2009-04-30 |archive-date = 2012-03-04 |archive-url = https://web.archive.org/web/20120304173001/http://www.netflixprize.com/assets/ProgressPrize2007_KorBell.pdf  }}</ref>
<blockquote>
Predictive accuracy is substantially improved when blending multiple predictors. ''Our experience is that most efforts should be concentrated in deriving substantially different approaches, rather than refining a single technique.''  Consequently, our solution is an ensemble of many methods.</blockquote>

Many benefits accrued to the web due to the Netflix project. Some teams have taken their technology and applied it to other markets. Some members from the team that finished second place founded [[Gravity R&D]], a recommendation engine that's active in the [[ACM Conference on Recommender Systems|RecSys community]].<ref name="nytimes.com"/><ref>{{cite web|last1=Bodoky|first1=Thomas|title=Mátrixfaktorizáció one million dollars|url=http://index.hu/tech/net/2009/08/07/matrixfaktorizacio_egymillio_dollarert/|website=Index|date=2009-08-06}}</ref> 4-Tell, Inc. created a Netflix project–derived solution for ecommerce websites.

A number of privacy issues arose around the dataset offered by Netflix for the Netflix Prize competition. Although the data sets were anonymized in order to preserve customer privacy, in 2007 two researchers from the University of Texas were able to identify individual users by matching the data sets with film ratings on the [[IMDb|Internet Movie Database (IMDb)]].<ref>[https://www.wired.com/science/discoveries/news/2007/03/72963 Rise of the Netflix Hackers] {{webarchive |url=https://web.archive.org/web/20120124011808/http://www.wired.com/science/discoveries/news/2007/03/72963 |date=January 24, 2012 }}</ref> As a result, in December 2009, an anonymous Netflix user sued Netflix in Doe v. Netflix, alleging that Netflix had violated United States fair trade laws and the [[Video Privacy Protection Act]] by releasing the datasets.<ref>{{cite magazine|url=https://www.wired.com/2009/12/netflix-privacy-lawsuit/|title=Netflix Spilled Your Brokeback Mountain Secret, Lawsuit Claims|date=17 December 2009|magazine=WIRED|access-date=31 March 2025}}</ref> This, as well as concerns from the [[Federal Trade Commission]], led to the cancellation of a second Netflix Prize competition in 2010.<ref name="nfcancel">{{cite web | url = http://blog.netflix.com/2010/03/this-is-neil-hunt-chief-product-officer.html | title = Netflix Prize Update | date = 2010-03-12 | publisher = Netflix Prize Forum | access-date = 2011-12-14 | archive-date = 2011-11-27 | archive-url = https://web.archive.org/web/20111127084829/http://blog.netflix.com/2010/03/this-is-neil-hunt-chief-product-officer.html  }}</ref>