Recommender Algorithms

From wikipedia

K-Nearest Neighbor

One of the most commonly used algorithms in recommender systems is the k-nearest neighborhood (k-NN) approach.^[16] 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.

Pearson Correlation

The Pearson Correlation is a measure of the correlation (linear dependence) between two variables X and Y, giving a value between +1 and −1 inclusive. In a social network, a particular user's neighborhood with similar taste or interest can be found by calculating the Pearson correlation coefficient. By collecting the preference data of top-N nearest neighbors of a particular user (weighted by similarity), the user's preference can be predicted.

Rocchio Relevance Filtering

Rocchio Classification is a method of relevance feedback dating back to the 1970s.^[17] Rocchio makes use of the Vector Space Model and is based on the assumption that most users have a general conception of which items should be denoted as relevant or non-relevant. User feedback is used to refine a search query by emphasizing or deemphasizing certain terms (similar to how Pandora refines its user recommendations). Through feedback, the user's search query is revised to include an arbitrary percentage of relevant and non-relevant terms as a means of increasing the search engine's recall, and possibly the precision as well. The number of relevant and non-relevant terms allowed to enter a query is dictated by a series of weights in the central equation.


References
•http://en.wikipedia.org/wiki/Recommender_system