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From Pat Ferrel <...@occamsmachete.com>
Subject Re: Recommending for new products
Date Sun, 04 Dec 2016 01:54:44 GMT
PredictionIO is not a recommender. It is a Framework you add templates to. The templates do
all the machine learning and can be of many types. 

That said if you are using the Universal Recommender here: https://github.com/actionml/universal-recommender
<https://github.com/actionml/universal-recommender> the user’s actions in real-time
are used to make recommendations—no need to have them in training. 

However any item to be recommended must be known at `pio train` time. If you have some items
that have no events you should provide another method for getting events. Think of Recommenders
as part of discovery, which usually includes browsing by category or other property, search,
and recommend. Both browse and search will allow users to find items without events and in
doing so create events for them.

The Universal Recommender adds random recs and the ability to boost items by properties and
so can recommend when the are no events associated with items. This should not replace browse
or search but can be of some help with new items.

Docs here: http://actionml.com/docs/ur <http://actionml.com/docs/ur>


On Dec 2, 2016, at 12:34 PM, Harry Li <HLi@loyalty.com> wrote:

Hello,
 
I am sort of confused on how PIO handles the recommendations for products that were not in
our training.
I understand for handling new users, it recommends the most popular products
 
Harry


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