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The Netflix Prize

Netflix released over 100 million ratings and offered one million dollars to any team that beat its Cinematch recommender by a further 10 percent. The contest framed recommending as predicting star ratings, a framing Netflix later moved away from.

October 2006. Launch of the contest. The grand prize was awarded in 2009. Chronology section: 1990 to 2009.

CaseDocumentedPlatforms

Where it sits: Consumer applicationUse of psychology, or claims about it, in commerce, policy or product design.
Evidence level: DocumentedThe event or practice is documented by primary or authoritative sources.
Organizations: NetflixAn organization is listed where the sources concern it. The text says exactly how.
Sources inspected: 4 of 42 with the relevant text, company document or archive item checked, 2 as an abstract or reference article, 0 not verified.

What happened

In October 2006 Netflix released over 100 million ratings, with their dates, from over 480 thousand anonymous subscribers on nearly 18 thousand titles, collected between October 1998 and December 2005. It offered a one million dollar Grand Prize to the team that improved the accuracy of its Cinematch recommender by a further 10 percent. A team called BellKor's Pragmatic Chaos won in 2009.

The psychological idea

The contest was an open prediction competition. Accuracy was scored by the root mean squared error between predicted and actual star ratings on over 3 million withheld recent ratings. Netflix reported Cinematch at 0.9514 on the quiz subset, 9.6 percent better than predicting each movie's average. The framing treated recommending as the problem of predicting how many stars a person would give a film.

In practice

Netflix authors say the company relied heavily on star-rating prediction when its main business was DVDs by mail, and that algorithms from the competition are still used in production to predict ratings. A 2012 news report quotes Netflix as saying it tested the newer methods offline and the gains did not seem to justify the engineering effort, and that its focus had moved on. These are company statements, not independent findings.

Why it mattered

The prize was a public release of a large body of customer ratings, together with a recommendation problem, to outside researchers. Netflix's own paper reported that by June 2007 over 20 thousand teams had registered. Netflix's later account also describes a change in the task: from predicting star ratings toward using what members actually watch.

Limits and qualifications

Accuracy on held-out star ratings is a narrow measure of a recommender, and Netflix itself says viewing data now matter more than stars. The account of why the winning algorithm was not deployed comes from Netflix as quoted by BGR; the original 2012 blog post could not be opened. The data release led to a re-identification study and a lawsuit.

Evidence behind the claims

In October 2006 Netflix released over 100 million ratings with dates from over 480 thousand anonymous subscribers on nearly 18 thousand titles, collected October 1998 to December 2005, and withheld over 3 million recent ratings as a qualifying set.
DocumentedSource: [1]Where: Section 2, The Netflix Prize
Netflix reported Cinematch at RMSE 0.9514 on the quiz subset, 9.6 percent better than predicting movie averages, and set the Grand Prize for a further 10 percent improvement.
DocumentedSource: [1]Where: Section 2
MIT Technology Review reported in October 2006 a one million dollar prize for increasing the accuracy of the Netflix recommendation system by 10 percent by 2011, with an annual 50,000 dollar progress prize.
DocumentedSource: [2]Where: Article text as returned by the fetch toolChecked at page level
The 2009 grand prize went to a team called BellKor's Pragmatic Chaos.
DocumentedSource: [3]Where: Article textChecked at page level
BGR quotes Netflix as saying it evaluated the new methods offline and the additional accuracy gains did not seem to justify the engineering effort, and that its focus on personalization had shifted by then.
Company self-reportSource: [3]Where: Article text quoting Netflix's 2012 blog post, which was not openedChecked at page level
Netflix authors say recommending was historically treated as predicting star ratings, that algorithms from the competition are still used in production to predict ratings, and that viewing data now give much better ways to help people find videos than focusing on a high predicted star rating.
Company self-reportSource: [4]Where: Section 2, The Netflix Recommender System
By June 2007 over 20 thousand teams had registered from over 152 countries.
DocumentedSource: [1]Where: Section 5, Prize status, June 2007

People involved

James Bennett. Netflix, co-author of the 2007 description of the Prize
Stan Lanning. Netflix, co-author of the 2007 description of the Prize

In the Psy101 course

OpenStax Psychology 2e, section 2.3: Analyzing FindingsChapter 2, Psychological Research. Context. The section discusses this topic.Opened the page: it says correlations have predictive value, with a grades example, which is the loose link to predicting ratings from past data.
OpenStax Psychology 2e, section 6.3: Operant ConditioningChapter 6, Learning. Context. Nearest related section; it does not discuss this topic by name.Nearest related section: operant conditioning. The Prize concerns prediction accuracy, not conditioning.

This is course relevance only. It is not evidence for the historical or corporate claims on this page.See the full course map.

Sources and inspection

4 of 4 sources were opened and read at the level shown. A source marked as not verified supports no claim on this page by itself.

[1]
The Netflix PrizeBennett, J; Lanning, S. Proceedings of KDD Cup and Workshop 2007 (ACM), 2007. Journal article. Company document.Company document readRead all four pages of the Netflix-authored paper (abstract, section 2 on the Prize, section 3, section 5 on status in June 2007) from the PDF.
[2]
The $1 Million Netflix ChallengeMIT Technology Review, 2006. News report. Secondary account.Reference article readRead the 2006 news article as returned by the fetch tool: prize terms, 2011 deadline, progress prize and the 103 million and 100 million rating figures.
[3]
Netflix paid 1 million for a recommendation algorithm it never usedBGR, 2012. News report. Secondary account.Reference article readRead the news report as returned by the fetch tool. It quotes Netflix's April 2012 blog post; the reviewer could not open the Netflix post itself (HTTP 403 for netflixtechblog.com), so the quoted wording rests on BGR alone.
[4]
The Netflix Recommender System: Algorithms, Business Value, and InnovationGomez-Uribe, C; Hunt, N. ACM Transactions on Management Information Systems, 2015. Journal article. Company document.Company document readRead pages 1 to 10 of the 19-page PDF copy (abstract, sections 1 to 4.2). Written by Netflix employees; the copy is hosted by gwern.net and carries the ACM publication line, December 2015.

Access note: Netflix's 2012 blog post on the unused winning algorithm returned HTTP 403 for both the researcher and the reviewer; its wording is taken from one news report (BGR). Original press release not opened.

Independent source review: Claude review agent, independent of the research agent, 2026-10-21. Verdict: pass-with-edits.