Thank you! I will give UR a try.
On Wed, Sep 6, 2017 at 1:51 AM, Pat Ferrel wrote:
> 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 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
>