This is a general problem with these "machine learning contests" for complex online learning systems.
There is no realistic way to translate all the complexity of ML production environment into a neatly packaged problem for contestants. You have to cut down dataset size, drops features, simplify targets and trivialize the implementation requirements (computational complexity, prediction latency, engineering cost). Not to mention that you have to expend significant effort to prepare / normalize / sanitize your dataset and even then you could still get hit with a privacy lawsuit, just like Netflix did.
The result of the contest at best is a set of "ideas" that you have to review/study before starting your own implementation / experimentation from scratch. Did Netflix prize pay for its cost? Perhaps the biggest benefit was generating publicity and helping with data scientist recruiting.
There is no realistic way to translate all the complexity of ML production environment into a neatly packaged problem for contestants. You have to cut down dataset size, drops features, simplify targets and trivialize the implementation requirements (computational complexity, prediction latency, engineering cost). Not to mention that you have to expend significant effort to prepare / normalize / sanitize your dataset and even then you could still get hit with a privacy lawsuit, just like Netflix did.
The result of the contest at best is a set of "ideas" that you have to review/study before starting your own implementation / experimentation from scratch. Did Netflix prize pay for its cost? Perhaps the biggest benefit was generating publicity and helping with data scientist recruiting.