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From Pat Ferrel <>
Subject Re: Use of latent informations associated to items with Mahout's SimilarityAnalysis.cooccurrences
Date Fri, 02 Jun 2017 15:47:01 GMT
When a user expresses a preference for a tag, word or term as in search or even in content
like descriptions, these can be considered secondary events. The most useful are tags and
search terms in our experience. Content can be used but each term/token needs to be sent as
a separate preference while search phrases can be used though again turning them into tokens
may be better.

Please looks through the docs here: or the siide deck here:

The major innovation of CCO, the algorithm behind the UR, is the use of these cross-domain
indicators. They are not guaranteed to predict conversions but the CCO algo tests them and
weights them low if they do not so we tend to test for strength of prediction of the entire
category of indictor and drop them if weak or set a minLLR threshold and filter weak individual
indicators out.

Technically these are not called latent, that has another meaning in Machine Learning having
to do with Latent Factor Analysis.

On Jun 1, 2017, at 11:26 PM, Marius Rabenarivo <> wrote:

Hello everyone!

Do you have an idea on how to use latent informations associated to items like tag, word vector
embedding in Mahout's SimilarityAnalysis.cooccurrences?



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