Hi guys, thanks for sharing your experiences on this subject, really
appreciated. To summarize the discussion:
- The decay of old preference values might loose important historical data
in cases where the user has no recent activity (Gokhan)
- When using decay (or truncate preferences), the precis
ommendation
> space
> > sparse, thus making it hard to find good recommendations (ie
> > recommendations which are relevent and not obsolete). If so, it might be
> > worth considering simpler approaches than CF.
> >
> >
> > Sigbjørn
> >
> >
> >
Assuming that most recent ratings or implicit preference data is more
important than the older ones, I wonder if there is a way to decrease the
importance (score) of old preference entries without having to update all
previous preferences.
Currently I'm fetching new preferences from time to time a
arrett Strausser <
> j.barrett.straus...@gmail.com
> > wrote:
>
> > You can look at : Flume, Oozie, Mesos, Chronos, Luigi.
> >
> >
> > On Wed, Oct 30, 2013 at 4:19 PM, Ted Dunning
> > wrote:
> >
> > > No. Scheduling is outside of
I wonder if Mahout (more precisely
org.apache.mahout.cf.taste package) has any helper class to execute
scheduled tasks like fetch data, compute similarity, etc.
Thank you
Cassio
Hi all,
I have a product recommendation use case for an e-commerce site and I've
been playing around mahout's CF capabilities lately. I reached a point
where I should ask a feedback from the community on my approach. I'm
struggling to get ANY recommendation.
Here's what I've done so far:
1) Coll
