Collaborative filtering
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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! |
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. Computing the correlations between pairs of users to identify a user’s neighbors in taste space 3. Combine the ratings of these neighbors to make recommendations.