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From Mario Levitin <>
Subject Top-N recommendation with matrix factorization
Date Fri, 20 May 2016 10:59:30 GMT

If one is using a matrix factorization based method, in order to generate a
top-N recommendation to a user, all the unknown ratings of that user needs
to be predicted (so that highest predicted N items can be recommended). If
we are talking about a site with millions of items this means that to make
a top-N recommendation to a user, that user's rating on millions of items
need to be predicted. This seems rather an inefficient way. I have two

First one is general: do you know how this is can be done in a more
efficient way, or how real large sites do this.
Second, how can I do this efficiently with Mahout.


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