How do current recommendation systems work?

In online recommendation systems typically the simplest solutions are the most effective. And probably the very simplest of all - and the one that generates significant lift for most providers of on-line recommendation technology is the K-Nearest Neighbor approach.


K-nearest neighbor classification

One of the most commonly used algorithms in recommender systems is the k-nearest neighborhood (k-NN) approach. The k-NN algorithm is a method for classifying objects based on the properties of its closest neighbors in the feature space. In k-NN, an object is classified through a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of its nearest neighbor.