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: http://actionml.com/docs/ur or the siide deck here: https://www.slideshare.net/pferrel/unified-recommender-39986309 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 <[email protected]> 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? Regards, Marius -- You received this message because you are subscribed to the Google Groups "actionml-user" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected] <mailto:[email protected]>. To post to this group, send email to [email protected] <mailto:[email protected]>. To view this discussion on the web visit https://groups.google.com/d/msgid/actionml-user/CAC-ATVEO_YON-5E95iPJjBR-FUgEv8TQsOA0rtD-xg0u-tNA_g%40mail.gmail.com <https://groups.google.com/d/msgid/actionml-user/CAC-ATVEO_YON-5E95iPJjBR-FUgEv8TQsOA0rtD-xg0u-tNA_g%40mail.gmail.com?utm_medium=email&utm_source=footer>. For more options, visit https://groups.google.com/d/optout <https://groups.google.com/d/optout>.
