Inside Word: What The Netflix Prize Says About The Shortfalls Of Ad-Targeting Startups

Andrew Chen

The Inside Word is a weekly feature that looks at compelling industry debates and discussions unfolding on the blogs of employees at digital-media companies.

Blogger: Andrew Chen

Position: A self-described entrepreneur-out-of residence, Chen is a former entrepreneur-in-residence at early-stage VC firm Mohr Davidow Ventures and was a director of product marketing at behavioral ad targeting firm AudienceScience.

Blog name: Andrew Chen

Backstory: In October 2006, Netflix (NSDQ: NFLX) offered a $1 million prize to anybody who could build an algorithm that would improve on its system of predicting a user’s movie ratings by 10 percent. Last month, Netflix declared a winner, handing out the prize money to a team that had beat its algorithm by 10.5 percent.

Blog post: In a blog post, Chen writes that the story of the prize should put a damper on ad networks’ claims that they can distinguish themselves by offering better ad-targeting technology. “This means if you combine dozens of the best machine-learning people in the world, some of the cleanest datasets, you get a measly 10.5 percent increase,” he writes. “Compare this to starting a new ad network where you end up with noisy datasets, lots of crappy traffic, and a small team looking at the problem

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