User similarity

It is also known as user-user collaboration filtering. It is a collaborative filtering systems based on rating similarity between users. For example news articles sites, build user profile based on your past browsing history and map to particular user bucket. After that recommend news articles for you by computing user similarity metrics.

There are some problems with user-to-user collaborative filtering.

a.   Systems performed poorly when they had many items but comparatively few ratings

b.   Computing similarities between all pairs of users was expensive

c.    User profiles changed quickly and the entire system model had to be recomputed

Mahout provides following implementations for user-to-user collaborative filtering. Following classes implements UserSimilarity interface. UserSimilarity finds similarity between two users.

a.   CachingUserSimilarity

b.    CityBlockSimilarity

c.     EuclideanDistanceSimilarity

d.    GenericUserSimilarity

e.    LogLikelihoodSimilarity

f.      PearsonCorrelationSimilarity

g.    SpearmanCorrelationSimilarity

h.   TanimotoCoefficientSimilarity
i.      UncenteredCosineSimilarity

Prevoius                                                 Next                                                 Home