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Recommender systems, Part 1: Introduction to approaches and algorithms
Most large-scale commercial and social websites recommend options, such as products or people to connect with, to users. Recommendation engines sort through massive amounts of data to identify potential user preferences. This article, the first in a two-part series, explains the ideas behind recommendation systems and introduces you to the algorithms that power them. In Part 2, learn about some open source recommendation engines you can put to work.
Also available in: Russian   Japanese  
Articles 12 Dec 2013
Recommender systems, Part 2: Introducing open source engines
Part 1 of this series introduces the basic approaches and algorithms for the construction of recommendation engines. This concluding installment explores some open source solutions for building recommendation systems and demonstrates the use of two of them. The author also shows how to develop a simple clustering application in Ruby and apply it to sample data.
Also available in: Russian   Japanese  
Articles 12 Dec 2013

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