Sunday, 17 February 2013

Apache Mahout

Collaborative filtering (CF) is a technique used by some recommender systems. Collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue x than to have the opinion on x of a person chosen randomly.

Currently Mahout supports mainly four use cases: 

  1. Recommendation mining takes users' behavior and from that tries to find items users might like. 
  2. Clustering takes e.g. text documents and groups them into groups of topically related documents. 
  3. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. 
  4. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

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