I would go so far as to say that all of the old Taste-oriented code is strongly deprecated. The indicator-based approach that Pat refers to is the best way forward.
On Thu, Feb 12, 2015 at 8:29 AM, Pat Ferrel <p...@occamsmachete.com> wrote: > The new cooccurrence recommender that works with a search engine has > several references at the top of the page here: > http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html > > On Feb 12, 2015, at 6:56 AM, John Hofmann <genghisu...@gmail.com> wrote: > > You're not missing any secret cache of mahout documentation as far as I > know. I learned what the recommender options were by looking through the > source code. They're spelled out there. If you know any C-based languages > you can navigate the code pretty easily but expect to spend some time > getting familiar with the repo. > > The other avenue I've taken was the book "Mahout in Action." It's a little > dated, but is generally still pretty applicable. It goes into more detail > about why you'd pick one option over another. I've noticed that most of > the blog posts about mahout assume you have a level of knowledge about > different ML algorithms that is comparable to what's in this book, so it's > a good one to read if you are going to be doing serious work with Mahout. > > There might be better options, but that's how I learned. Hope this helps! > > On Thu, Feb 12, 2015 at 9:24 AM, Eugenio Tacchini < > eugenio.tacch...@gmail.com> wrote: > > > Hi all, > > I am new to mahout, it has been a couple of days now since I started > > working with it and I've found it very very powerful. > > > > I noticed, however, a general lack of documentation. I am working just > with > > the recommender system features and, correct me if I am wrong, I can't > find > > anywhere some complete documentation about. All I can find in the > official > > website is some quick-start tutorials. > > > > Knowing the theory about recommender systems, I expect a few very simple > > documentation pages which tell me something like: > > > > - The algorithms implemented are: 1) user-based, 2) item-based, 3) > > .......... > > - To use 1) in your code, follow these steps: > > - choose a user similarity measures (available measures: 1) Pearson > > Correlation 2) Cosine similarity 3) ..... 4).......) > > - choose a neighbors selection techniques (available techniques 1) > > threshold 2) fixed number 3).... 4)....) > > - compute the neighbors using the following > > UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, > > similarity, dm);....... > > > > and so on until all the options available are covered. > > > > I didn't find anything like that, for example at the moment I am trying > to > > figure out how to compute a prediction (user-based algorithm), e.g. > predict > > the rating for user x movie y but I didn't find anything about that. > > > > Forgive me if there is something I am missing, I just want to focus on > the > > right content to learn about mahout, any suggestion is welcome. > > > > Thanks > > > > Regards, > > > > Eugenio Tacchini > > > >
