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Comment by pagetruif | Hacker News Reader

For people interested in the algorithms that won the Netflix Prize (including the Matrix Factorization and Restricted Boltzmann Machine approaches mentioned in the first section), there's a summary here: http://blog.echen.me/2011/10/24/winning-the-netflix-prize-a-...

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I often wondered if this amount of effort is truly required and if it actually provides results people can relate to. I used Netflix for a while and I find the recommendations to be pretty average for me. Quite possibly it is because I don't tend to rate most movies I watch. I wonder how often that is the case for others though.

More importantly I just don't think you can predict what I want to watch based on factors such as history, ratings, watch times etc. External factors such as mood, recommendation from friend (not on Netflix) and curiosity almost always play a large factor in choose the thing I watch next. These things are not known to Netflix.

As a semi-educated guess I would say that simply providing a listing of popular movies based on ratings/watch times per genre as well as similarity would be enough for most people. It is easier to understand and how many people want to watch something just because it is popular anyway?

That's why they show different rows from different sources. In my country Netflix is not available yet so I'm not sure about what I'm going to say, but I'm quite confident that one of those rows is just the top-rated/popular movies of the site.

To me, the Netflix price is as much about marketing as it is about the quality of the algorithm: Netflix was ahead of the pack (i.e. Blockbuster) even before, but after spreading the word that it takes rocket scientists to even slightly improve the existing algorithm, who would even think twice about testing a DVD/ streaming provider other than Netflix?

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