No it is not the same 'because' computation in the paper. The one in the paper is better. However it requires storing a k x k matrix for every user, or computing it on the fly, both of which are pretty prohibitive. They're not hard to implement though. This is a cheap-o non-personalized computation based on item similarity. No, the system does not serve the original data, just results from the factored model. It's assumed that, if the caller needs this info, the caller has it, and its purpose is generally not specific to the core recommender, so accessing this data is not part of the engine.
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