Tuesday, 1 September 2015

Item Similarity Vs User similarity


In this post, I am going to discuss about user similarity and item similarity concepts. Later posts explain various implementations for both Item similarity and user similarity.

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.

d.    GenericUserSimilarity

Item similarity
Item-item models use rating distributions per item, not per user. Suppose there are more users than items, each item tends to have more ratings than each user, so an item's average rating usually doesn't change quickly.

Mahout provides following implementations for item-to-item collaborative filtering. Following classes implements ItemSimilarity interface. ItemSimilarity finds similarity between two items.

d.    FileItemSimilarity
e.    GenericItemSimilarity



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