You can write your own ItemSimilarity metric based on the features and then use an item-based recommender. That piece you'd have to do yourself by making up some notion of similarity; if the features were all numeric and normalized you can look at repurposing something based on Euclidean distance or cosine similarity or something, but I don't know if those are the features you have.
Sean On Sat, May 12, 2012 at 2:15 AM, EDUARDO ANTONIO BUITRAGO ZAPATA <[email protected]> wrote: > Hi All, > > As far I've seen, all mahout recommenders uses this setting for input file: > > User1, item1, rating1 > User1, item2, rating2 > ... > User2, item1, rating3 > > What I need to do is a recommender for new digital cameras. I want to know > which user is more interested in a camera when it arrives, then I can make > recommendation. To do that, I want to take into account the camera > features (optical zoom, LCD size, etc). ¿Is there a way to implement this > in mahout? Maybe the input file is something like this: > > User1, item1, feat1, feat2, … , featn, rating1 > User1, item2, feat1, feat2, … , featN, rating2 > ... > User1, itemN, feat1, feat2, … , featN, rating3 > User1, newItem, feat1, feat2, … , featn, ? > > Any help would be appreciated. > > -- > Eduardo** <[email protected]>
