Collaborative filtering is a technique used by recommendation engines. According to Wikipedia, collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc.
For example, when you are visiting Amazon you see product suggestions. These suggestions are based on your history and the history of other users. The other users serve as “sensors” who help Amazon identify products you might like. Let’s use a concrete example from online dating. In this context, what could collaborative filtering look like?
As a user after expressing your interest for a few people you’d like to date, a collaborative filtering system could start suggesting you potential matches. You taste will start to match the taste of users A, B and C. The recommendation system will use it to provide you with potential dates. The suggested dates will be in priority the people that A, B and C have liked and that you haven’t seen yet.
This works as if A, B and C were browsing the site to find potential dates you’d like. Their “work” spare you sorting through thousand of irrelevant people. What makes this approach so powerful is that it gives a concrete, personal reality to the expression “the wisdom of crowds”. As an individual you can benefit from recommendation that are based on people you do not know and their preferences. Collaborative filtering is a way to provide concrete insights based with large data sets.