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 >
