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From Pat Ferrel <...@occamsmachete.com>
Subject Re: Recommender for social media
Date Tue, 05 Sep 2017 17:51:38 GMT
Actually IMO it is not more complex, it is just far better documented and more flexible. If
you don’t need the features it is just as simple as the Apache PIO Templates. I could argue
the UR is simpler since you don’t need to $set every item and user, they are determined
automatically from the data.

But in any case a recommender is a big-data application. 16GB on one machine will not get
you very far, maybe a POC with limited data.

The next question is what do you need. If you need to use all of those pieces of data to recommend
one thing, then the ALS algorithm of the Apache PIO Templates will not work, they can only
take one “conversion” event. This is ok for some applications but it would mean using
like alone to recommend other items. Not sure a create will work at all since the user may
be the only one to interact with the created item, unless there are types of metadata associated
with the created item. With the Apache Templates “follows” can only recommend users to
follow.

The UR can use both likes and follows to recommend either items or users. It’s also likely
that you can use other data you have. This may be what you mean by complex but then you don’t
have to use the feature...


On Sep 5, 2017, at 2:10 AM, Brian Chiu <brian@snaptee.co> wrote:

Hi everyone.

I am trying to use PredictionIO to build a recommender for
social-media-like platform, but as I am new to recommender I would
like to get some suggestion from the community here.

The case is something like Twitter:
- A user can create an item
- A user can like an item
- A user can follow another user

I have spent sometime trying the official templates, but it seems that
they cannot take advantage of "follow another user" relationship.  I
notice that the "Universal Recommender" from actionML is more powerful
than the official template, but also more complex, and I don't know if
it is suitable for my use case.

Is "Universal Recommender" right choice?  Or is there a simpler
solution?  My machine has 16GB memory and around 50,000 users.

Thanks in advance!

Best,
Brian


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