Hi Gawesh, you could also implement an ensemble recommender by using two recommenders and then blending the results.
The following JIRA is discussing how to do this: https://issues.apache.org/jira/browse/MAHOUT-810 /Manuel On 28.01.2012, at 17:42, gj wrote: > Hi, > > I am trying some experiments (mahout 0.5) with a new neighbourhood > model that includes implicit feedback as well as explicit feedback. > And I wanted to know, what are the clIasses, etc that I need to > modify. > > To start, my input to the system should in the following format: > > userid, itemid, exp_feedback, imp_feedback > > where userid and itemid are integers and exp_feedback and imp_feedback > are floats between 0.0 and 1.0 > for example: > > 101, 1, 0.67, 0.18 > > I've used the recommender system code in mahout before. But it will be > my first time trying to modify it in order to run some custom code. I > have skimmed through the Mahout in Action book. > > The other point is that I want to make this modifications with as > little changes to the mahout distribution so that I can benefit from > the optimisations that are present in the distribution. > > Definitely I need to have custom UserSimilarity and I need a custom > UserNeighborhood? What else? Can somebody please give me some pointers > where to start, e.g which classes i need to modify, etc. and what are > the things I should be wary of? Then I will have a go and report back > my progress. > > > Regards > Gawesh -- Manuel Blechschmidt Dortustr. 57 14467 Potsdam Mobil: 0173/6322621 Twitter: http://twitter.com/Manuel_B
