Thank you! I will give UR a try.
On Wed, Sep 6, 2017 at 1:51 AM, Pat Ferrel <p...@occamsmachete.com> 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 <br...@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 >
