Hi,
I am a newbie and am looking for some guidance to implement my recommender. Any help would be greatly appreciated. I have a small data set of location information with the following fields: neighborhood, amenities, and counts. For example: Downtown Gym 15 Downtown Cafe 50 … Midtown Gym 30 Midtown Cafe 100 Midtown Bookstore 10 ... Financial Dist … so on and so forth. I want to recommend a neighborhood for a user to reside base on the amenities (and some other metrics) in his/her current neighborhood. My understanding is that model-based recommendation would be a good fit for the job. If I am on the right track, is there a experimental/beta recommender I can try? If there is no such recommender yet, can I still use Mahout for my project? For example, can I implement my own Similarity which only computes the similarity between one user's preference to a set of neighborhood? If I understand Mahout correctly, User/Item Similarity would do N x (N-1) pair of comparisons as oppose to 1 x N comparisons. In my example, User/Item Similarity would compare between Downtown, Midtown, Fin Dist -- which would be a waste in computation resources since the comparisons are not needed. Thanks in advance for your help. Edith
