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You're not alone. I don't know if you have a ML background, but it has been somewhat widely discussed in the community that the way Netflix scored the competition -- RMSE -- isn't the best way. E.g. http://hunch.net/?p=949 and http://andrewgelman.com/2008/11/netflix_prize_s/

In the OPs article they mention they monitor if a movie is watched to completion, which gives them a much better metric to optimise. The other issue is that this is really a sequential decision making problem. Recommending a movie has an opportunity cost -- there are other movies you don't recommend -- and the recommendation is an ongoing process, so it is probably best to spend some time exploring the user's taste on the assumption this will let you make better recommendations in the future. Accounting for these issues is much harder in a competition format.




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