Collaborative filtering

All unapproved Citizendium articles may contain errors of fact, bias, grammar etc. A version of an article is unapproved unless it is marked as citable with a dedicated green template at the top of the page, as in this version of the 'Biology' article. Citable articles are intended to be of reasonably high quality. The participants in the Citizendium project make no representations about the reliability of Citizendium articles or, generally, their suitability for any purpose.

This article is currently being developed as part of an Eduzendium student project. The course homepage can be found at CZ:Special_Topics_2010.
To provide students with experience in collaboration, you are warmly invited to join in here, or to leave comments on the discussion page. The anticipated date of course completion is 13 August 2010. One month after that date at the latest, this notice shall be removed.
Besides, many other Citizendium articles welcome your collaboration!

Main Article

Discussion

Definition

A Collaborative Filtering(CF) refers to the use of software algorithms for narrowing down a large set of choices by using collaboration among multiple agents, viewpoints, and data sources.

Overview

The term Collaborative Filtering was first by the makers of one of the first recommendation systems, Tapestry. The basic assumption in CF is that user A and user B's personal tastes are co-related if both users rate n items similarly.

Collaborative Filtering systems follow this approach to produce recommendations:
1. Gather ratings from users or maintain user's ratings in a database.
2. Compute the correlations between pairs of users to determine a user’s neighbors in taste space
3. Combine the ratings of these neighbors to make recommendations.