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 <[email protected]>:

> 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 <[email protected]>
> wrote:
>
> Please correct side to size in my previous e-mail
>
> 2017-06-03 6:14 GMT+04:00 Marius Rabenarivo <[email protected]>:
>
>> 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 <[email protected]>:
>>
>>> 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 <
>>> [email protected]> 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 <[email protected]>:
>>>
>>>> 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 <
>>>> [email protected]> 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 <[email protected]>:
>>>>
>>>>> 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 <
>>>>> [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
>>>>>
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>>>>
>>>>
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