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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.
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.