If you are looking to recommend a similar neighborhood based on the characteristics of some other neighborhood (the user’s current one) so you wouldn’t use collaborative filtering. This is a metadata recommender based on similarity of neighborhoods not a collection of user preferences.
The easiest and fastest would be to use a search engine but I’ll leave that for now since it doesn’t account for feature weights as well. create a table like this: Neighborhood Gym Cafe Bookstore Downtown 15 50 0 Midtown 30 100 10 … You will need to convert the row IDs into sequential ints, which Mahout uses for IDs. Then read them into a sequenceFile creating a Distributed Row Matrix, which has Key - Value pairs. Keys = the integer neighborhood IDs, the Value is a Vector (a sort of list) of column integer IDs with the counts. Then run rowsimilarity on the DRM. This is the CLI but there is also a Driver you can call from your code. There are some data prep issues you will have since larger neighborhoods will have higher counts. An easy thing to do would be to normalize the counts by something like population or physical size so you get cafes per resident or per sq mile or some other ratio. The result of the rowsimilarity job will be another DRM of key = neightborhood ID, values = Vector of similar neighborhoods (by integer ID) with a strength of similarity. Sort the vector by strength and you’ll have an ordered list of similar neighborhoods for each neighborhood. On Jun 30, 2014, at 12:48 PM, Edith Au <[email protected]> wrote: 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
