Netflix competition is over?

The Netflix competition is a $1 million research competition to improve the Netflix movie recommender system by 10%. A large team called BellKor’s Pragmatic Chaos just announced that they won (update: unless someone can beat them in the next month). Among them is Yehuda Koren with whom I organized the 2nd Netflix-KDD Workshop and also some engineers from my home town (Piotte and Chabbert). I do not know how they will split the money, but I suspect each one of them will get at least 100k$. 

I want a study on the benefits of this new technology on the Netflix users.

Reference: See my older posts Proceedings of the Large-Scale Recommender Systems workshop and Netflix: an interesting Machine Learning game, but is it good science?

7 thoughts on “Netflix competition is over?”

  1. Congratulations to the winners.
    I realize that the end product resulting
    from this competition
    (a better recommendation system)
    is a great achievement by itself.
    Does anybody know what (if any)
    are more general benefits for machine learning/statistics/etc. which came out of this competition?
    Did people come up with better algorithms/models/implementations/other insights which could be used for other problems?

  2. Anonymous #3 — I haven’t seen anything about the final winning method, but what I understand from the previous progress prize report is that the biggest take-away from the Netflix competition is that a try everything, kitchen sink-style approach, combined with intelligent higher-level ensemble methods to combine the simpler models is very effective at squeezing the most out of the data.

    I’m not aware of any super novel approaches that came out of the competition (that were effective, at least). Does anybody know otherwise?

  3. Whether the very precise question asked by the Netflix Prize is directly relevant to improving the user recommendation experience is very interesting. MediaUnbound is doing a full series on the underlying issues and assumptions of the contest called Countdown to 10% here.

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