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From Pat Ferrel <...@occamsmachete.com>
Subject Re: UR: build/train/deploy once & querying for 3 use cases
Date Wed, 09 May 2018 13:41:13 GMT
Why do you want to throw away user behavior in making recommendations? The
lift you get in purchases will be less.

There is a use case for this when you are making recommendations basically
inside a session where the user is browsing/viewing things on a hunt for
something. In this case you would want to make recs using the user history
of views but you have to build a model of purchase as the primary indicator
or you won’t get purchase recommendations and believe me recommending views
is a road to bad results. People view many things they do not buy, putting
only view behavior that lead to purchases in the model. So create a model
with purchase as the primary indicator and view as the secondary.

Once you have the model use only the user’s session viewing history in the
as the Elasticsearch query.

This is a feature on our list.


From: gerasimos xydas <gerasimos.xydas@gmail.com>
<gerasimos.xydas@gmail.com>
Reply: user@predictionio.apache.org <user@predictionio.apache.org>
<user@predictionio.apache.org>
Date: May 9, 2018 at 6:20:46 AM
To: user@predictionio.apache.org <user@predictionio.apache.org>
<user@predictionio.apache.org>
Subject:  UR: build/train/deploy once & querying for 3 use cases

Hello everybody,

We are experimenting with the Universal Recommender to provide
recommendations for the 3 distinct use cases below:

- Get a product recommendation based on product views
- Get a product recommendation based on product purchases
- Get a product recommendation based on previous purchases and views (i.e.
users who viewed this bought that)

The event server is fed from a single app with two types of events: "view"
and "purchase".

1. How should we customize the query to fetch results for each separate
case?
2. Is it feasible to build/train/deploy only once, and query for all 3 use
cases?


Best Regards,
Gerasimos

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