Thursday, February 28, 2008

Hiring a researcher at Netflix

Here is the job specification for a research oriented position working for me at Netflix.

We are looking for a very experienced engineer who can take the lead, researching, modeling, presenting, designing and building algorithms that run in a service oriented architecture to support millions of customers. The successful candidate will be a self-motivated, intellectually curious individual. A Doctorate or experience working in a research environment, work with personalization algorithms, machine learning, adaptive systems, and statistical analysis would be a bonus.

I manage a sister group to the one that does the Cinematch star ratings predictions and hosts the Netflix Prize, we use that data as an input along with everything else we know about movies and customers to come up with the actual lists of movies that are shown all over the site. The research position in my group works on algorithms and mining for new data sources.

Also, Netflix is a great place to work, big enough to be interesting and small enough to be fun.

2 comments:

  1. Adrian, the 5 point rating scale may be a limitation to improvement in an algorithm predicting customer ratings.

    Since Netflix customers rarely rent, and rate, films they don't like, the 5-point scale is functionally a 3-point scale (3, 4, and 5).

    The 5-point scale, with its 3 points that are used most of the time, and the other 2 that are rarely used, could be converted, without losing existing ratings data, by adding the values 1.5, 2.5, 3.5, and 4.5.

    Then Netflix would have a 9-point scale, of which 7 values would be frequently used, and thus allow quantitative models to predict with greater accuracy than now.

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  2. Hi Vern, this was suggested and discussed a while ago on the Netflix blog. I agree that more resolution in the input should give better results, all else being equal. However, all else is not equal and Netflix did test half-stars a while ago, and found that it adversely affects the way people enter ratings, and we get better results over-all when users can only enter whole stars. This is a good example of the way Netflix does development. Intuition is used to come up with a plausible improvement, but measurements are used to refine, confirm or reject that intuition.

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