Comments away. I was a bit confused by the use of Vector[] for w1 and Matrix[] for inputs.
Robin Anil | Software Engineer | +1 312 869 2602 | Google Inc. On Thu, Apr 11, 2013 at 10:00 AM, Gokhan Capan <gkhn...@gmail.com> wrote: > Ted, > Robin, > > Although I did not test on a dataset yet, recently I've been implementing > Factorization Machines with SGD optimization. > > The initial implementation is at https://github.com/gcapan/mahout/tree/fm > > Would you guys consider to take a look so I can make it better and running? > > > > On Mon, Apr 1, 2013 at 8:45 PM, Nkechi Nnadi <nkechi.nn...@gmail.com>wrote: > >> Hello, >> >> I'm long time lurker. I would be interested in implementing these. I >> thought I would get my feet wet with contributing to wiki with tutorials >> since I have used Mahout for recommendation and clustering in my >> dissertation. I have never contributed code before and I would love to >> start now. >> >> -Nkechi >> >> >> On Sun, Mar 31, 2013 at 1:14 PM, Robin Anil <robin.a...@gmail.com> wrote: >> >> > FMs work really well for a whole range of things. Having implemented >> them >> > myself, I can extend my services as a reviewer if anyone is willing to >> > start on it. >> > >> > Robin Anil | Software Engineer | +1 312 869 2602 | Google Inc. >> > >> > >> > On Sun, Mar 31, 2013 at 2:18 AM, Ted Dunning <ted.dunn...@gmail.com> >> > wrote: >> > >> > > Relative to Dan's recent mention of SOM as possible new project, here >> are >> > > slides from KDD Cup 2012 in which Stephen Rendle describes how he did >> > using >> > > a very straightforward implementation of Factorization Machines [1,2]. >> > > >> > > >> > > FMs are interesting in the context of Mahout because they can be used >> in >> > a >> > > wide variety of settings including recommendation and targeting and >> > because >> > > they have very good performance on a number of tasks. >> > > >> > > I should mention that Robin was the one who first mentioned FMs to me. >> > > >> > > The KDD 2012 competition [3] is of interest in any case because it >> > provides >> > > a large amount of realistic data for commercially important problems. >> > > >> > > [1] >> > > >> > > >> > >> https://kaggle2.blob.core.windows.net/competitions/kddcup2012/2748/media/RendleSlides.pdf >> > > >> > > [2] >> > > >> > > >> > >> https://kaggle2.blob.core.windows.net/competitions/kddcup2012/2748/media/Rendle.pdf >> > > >> > > [3] http://www.kddcup2012.org/ >> > > >> > >> > > > > -- > Gokhan >
