Another great contribution would be small or mid-sized datasets and
gold master output sets for some of the standard computations. This
problem requires both gold masters and evaluation algorithms for
numerical variations against the masters.
This would be very educational about how Recommenders,
I think it's far from complete or done.
I think it would be interesting to take any of the MapReduce-based jobs, set
it up, run it, and benchmark/profile it to locate some bottlenecks, then
propose optimizations. It is a good way to get familiar with the packages.
You might also investigate
I'd be very interested in benchmark data for and/or performance
increases of RecommenderJob (as well as ItemSimilarityJob and
RowSimilarityJob which are used internally), if you feel like working on
that.
A good starting point to get familiar with the functionality might be
Sean's talk from
Thanks Sean and Sebastian.
Yes, it's still far away, just finished documentation stuff.
I will go though these stuff (Thanks for the links Sebastian) and try to get
familiar with Mahout. After that I can go in to your suggestions one by
one.
On Thu, Jan 20, 2011 at 1:46 PM, Sebastian Schelter
Hi Sean,
Thanks for the immediate reply and sorry for my late response.
Our above mentioned project is in progress.
BTW I realized that Mahout is quite interesting and very active project. I
am just interested about contributing to Mahout. As understanding the
complete code base is not an easy
Hi Kasun,
If you want to get involved, you are free to discuss and propose your own
changes and algorithms. You can review the list of open issues here:
https://issues.apache.org/jira/browse/MAHOUT This contains some ideas about
work that needs to be done.
One interesting project would be to
Hi all,
I am Kasun Lakpriya from University of Moratuwa, Sri Lanka. I am following a
BSc in Computer Science and Engineering degree and now I am in my final
year.
In our degree program in order to complete the degree we need to do some
kind of a research project approved by the university. The
