Mahout-Recommendation of Users

Question

I'm trying to find a way, using apache Mahout, to recommend similar users and not Items. I have a list of Users each of them have read certain books. I wanted to ask if there is a way to recommend a group of users to another user based on what he read.
As you can understand, the recommended users would have read some of the same books.
Thanks for your help and your guidance.

Answer 1

Use spark-rowsimilarity job in Mahout v1. Create a file of

user-ID<tab>book-ID1<space>book-ID2<space>etc...

In other words each row is a user's history of books read. First column is the user-ID, second column is a space delimited list of book-IDs. Run "mahout spark-rowsimilarity" and you'll get back files of the form:

user-ID<tab>user-ID5:strength<space>user-ID6:strength<space>etc...

This is a list of similar users for each user. The list is sorted and the strength is the LLR (log-likelihood ratio) score for how similar the users are.

Docs here: http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html

Answer 2

In Java you could do it like this:

org.apache.mahout.cf.taste.model.DataModel dataModel;
...
UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.75, similarity, dataModel);
UserBasedRecommender userBasedRecommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
long[] mostSimilarUserIDs = userBasedRecommender.mostSimilarUserIDs(...);