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From Marius Rabenarivo <mariusrabenar...@gmail.com>
Subject Re: Use of latent informations associated to items with Mahout's SimilarityAnalysis.cooccurrences
Date Sun, 04 Jun 2017 04:14:41 GMT
And what the T in the slides is for?

How can we implement it if it's is not implemented yet?

2017-06-04 8:11 GMT+04:00 Pat Ferrel <pat@occamsmachete.com>:

> Buy purchasing an item with a tag that you have given it, they are
> displaying a preference for that tag.
>
>
> On Jun 3, 2017, at 12:36 PM, Marius Rabenarivo <mariusrabenarivo@gmail.com>
> wrote:
>
> So the tag here is assumed to be a tag given by the user to an item?
>
> I was thinking that it was some kind of tag we give to the item by some
> mean (classification, LDA, etc)
>
> 2017-06-03 21:14 GMT+04:00 Pat Ferrel <pat@occamsmachete.com>:
>
>> 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>:
>>
>>> 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>:
>>>
>>>> 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> 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>:
>>>>
>>>>> 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> 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>:
>>>>>
>>>>>> 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.n
>>>>>> et/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> 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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>>>>>
>>>>>
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