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From Pat Ferrel <...@occamsmachete.com>
Subject Re: Use of latent informations associated to items with Mahout's SimilarityAnalysis.cooccurrences
Date Sat, 03 Jun 2017 17:14:47 GMT
A = history of all purchases (in the e-com case)
B = history of all tag preferences

r = [A’A]h_a + [A’B]h_b

The part in the slides about content-based recs is not needed here because you have captured
them as user preferences.


On Jun 2, 2017, at 7:22 PM, Marius Rabenarivo <mariusrabenarivo@gmail.com> wrote:

Please correct side to size in my previous e-mail

2017-06-03 6:14 GMT+04:00 Marius Rabenarivo <mariusrabenarivo@gmail.com <mailto:mariusrabenarivo@gmail.com>>:
What will be the size of the matrix if we send an event like tag-pref 
We will get a |U|x|T| matrix I think (where T is the set of all tags).

So [AtA] will be a |T| x |T| matrix and we will do a dot product with the user history hT
to get recommendation right?

I was assuming that A should be of side |U| x |I| where I is the set of all items as it should
be added to other terms of the whole enchilada formula afterwards.

Thank you for your guidance Pat.

2017-06-02 21:35 GMT+04:00 Pat Ferrel <pat@occamsmachete.com <mailto:pat@occamsmachete.com>>:
Please refer to the documents. The “event” is the name of the type of event or indicator
if preference, it implies the type of the targetEntityId. So a “tag-pref’ event would
be accompanied by a targetEntityId = tag-id. This is separate from attaching “tag” properties
to items with the $set event for use with filter and boost rules. One looks at the data as
a possible preference indicator and the other is used to restrict results. This is why we
usually name events so they sound like a user preference of some type, whereas item property
values are simply item attributes, intrinsic to the items and independent of an individual
user.

The event can have any name that makes sense to you.


On Jun 2, 2017, at 9:19 AM, Marius Rabenarivo <mariusrabenarivo@gmail.com <mailto:mariusrabenarivo@gmail.com>>
wrote:

so, the event field should be the token and targetEntityId the item ID, right?

2017-06-02 20:07 GMT+04:00 Pat Ferrel <pat@occamsmachete.com <mailto:pat@occamsmachete.com>>:
Yes, each is analyzed separately as a separate event. If you are using REST you can send up
to 50 events in a single array. Some SDKs may support this too.


On Jun 2, 2017, at 8:56 AM, Marius Rabenarivo <mariusrabenarivo@gmail.com <mailto:mariusrabenarivo@gmail.com>>
wrote:

So I have to send an event like category-preference for each tag associated to an item right?

entityId: userd-id
event: category-preference
targetEntityId : tag/token

2017-06-02 19:47 GMT+04:00 Pat Ferrel <pat@occamsmachete.com <mailto:pat@occamsmachete.com>>:
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 <http://actionml.com/docs/ur>
or the siide deck here: https://www.slideshare.net/pferrel/unified-recommender-39986309 <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 <mariusrabenarivo@gmail.com <mailto:mariusrabenarivo@gmail.com>>
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

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