What is Collaborative filtering?
Recommendation Systems are a widely accepted tool that can help users search, sort, filter and classify information. They incorporate recommender algorithms which generate personalised recommendations for users. The algorithms use different techniques to build user profiles and find patterns in the data.
The personalisation process has been incorporated into many well known e-commerce sites, and there are a number of those sites which use collaborative filtering methods to generate personal recommendations for users, including Amazon, GoogleNews and Facebook.
Collaborative Filtering
Collaborative (also known as Social) Filtering is an information filtering technique which uses individual user profiles and finds connections between them. The user profiles are constructed using subjective user or item criteria, such as ratings, the time spent viewing a page, and purchase history.
Recommendations are generated for a user using the data in other users' profiles as a pooled resource. The process is modelled on how people innately interact with each other i.e. sharing opinions. For example, if your close circle of friends have all seen the latest movie release and expressed a dislike for it you may not spend the money on going to see it. However, it may be the case that none of your friends like the same type of movies as you do. Fortunately advances in artificial intelligence and the Internet mean there is no restriction to our circle of friends when we want to find something of interest.