Thank you Pat!
So if I'm understanding correctly, I could set a user profile property as
follows:
{
"event" : "$set",
"entityType" : "user",
"entityId" : "u1234",
"properties" : {
"gender": "female"
},
"eventTime" : "2015-10-05T21:02:49.228Z"
}
Although this is not recommended. Right?
On 5 December 2017 at 17:38, Pat Ferrel <p...@occamsmachete.com> wrote:
> The User’s possible indicators of taste are encoded in the usage data.
> Gender and other “profile" type data can be encoded a (user-id, gender,
> gender-id) but this is used and a secondary indicator, not as a filter.
> Only item properties are used a filters for some very practical reasons.
> For one thing items are what you are recommending so you would have to
> establish some relationship between items and gender of buyers. The UR does
> this with user data in secondary indicators but does not filter by these
> because they are calculated properties, not ones assigned by humans, like
> “in-stock” or “language”
>
> Location is an easy secondary indicator but needs to be encoded with
> “areas” not lat/lon, so something like (user-id, location-of-purchase,
> country-code+postal-code) This would be triggered when a primary event
> happens, such as a purchase. This way locaiton is accounted for in making
> recommendations without your haveing to do anything but feed in the data.
>
> Lat/lon roximity filters are not implemented but possible.
>
> One thing to note is that fields used to filter or boost are very
> different than user taste indicators. For one thing they are never tested
> for correlation with the primary event (purchase, read, watch,…) so they
> can be very dangerous to use unwisely. They are best used for business
> rules like only show “in-stock” or in this video carousel show only video
> of the “mystery” genre. But if you use user profile data to filter
> recommendation you can distort what is returned and get bad results. We
> once had a client that waanted to do this against out warnings, filtering
> by location, gender, and several other things known about the user and got
> 0 lift in sales. We convinced they to try without the “business rules” and
> got good lift in sales. User taste indicators are best left to the
> correlation test by inputting them as user indicator data—except where you
> purposely want to reduce the recommendations to a subset for a business
> reason.
>
> Piut more simply, business rules can kill the value of a recommender, let
> it figure out whether and indicator matters. And always remember that
> indicators apply to users, filters and boosts apply to items and known
> properties of items. It may seem like genre is both a user taste indicator
> and an item property but if you input them in 2 ways they can be used in 2
> ways. 1) to make better recommendations, 2) in business rules. They are
> stored and used in completely different ways.
>
>
>
> On Dec 5, 2017, at 7:59 AM, Noelia Osés Fernández <no...@vicomtech.org>
> wrote:
>
> Hi all,
>
> I have seen how to use item properties in queries to tailor the
> recommendations returned by the UR.
>
> But I was wondering whether it is possible to use user characteristics to
> do the same. For example, I want to query for recs from the UR but only
> taking into account the history of users that are female (or only using the
> history of users in the same county). Is this possible to do?
>
> I've been reading the UR docs but couldn't find info about this.
>
> Thank you very much!
>
> Best regards,
> Noelia
>
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