Inside Word: What The Netflix Prize Says About The Shortfalls Of Ad-Targeting Startups
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
Very interesting insight, particularly from someone that designed products at Audience Science. Does this mean that Andrew believes that all behavioral targeting is pretty much bunk?
The push back here is that not all targeting is created equal. At Bizo we target specific business people based on their business demographic (bizographic) information, and the lifts can be significant (many cases well greater than 100%). Granted, we're not looking for behavior — just the demos of people that are a fit for B2B products and services. And to Andrew's point, we've found that the media is also important and the best lifts are created through a combination of data and media.
I notice all the interesting blog posts have been erased.
The netflix price simply proves that netflix algo was already state of the art.
Any one person can do a subset of recommendations better than an algorithm, of course, but you cannot do it at scale.
This is where people misunderstand the application of these algorithms and their utility.
Hi Magwa,
What interesting blog posts that have been erased are you referring to?
– Joe Tartakoff, paidContent.org