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* Collaborative filtering
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Collaborative and filtering
Collaborative filtering methods have been applied to many different kinds of data including sensing and monitoring data-such as in mineral exploration, environmental sensing over large areas or multiple sensors ; financial data-such as financial service institutions that integrate many financial sources ; or in electronic commerce and web 2. 0 applications where the focus is on user data, etc.
Collaborative filtering explores techniques for matching people with similar interests and making recommendations on this basis.
Collaborative filtering algorithms often require ( 1 ) users ’ active participation, ( 2 ) an easy way to represent users ’ interests to the system, and ( 3 ) algorithms that are able to match people with similar interests.
Collaborative filtering systems have many forms, but many common systems can be reduced to two steps:
Collaborative filtering approaches to build a model from a user's past behavior ( items previously purchased or selected and / or numerical ratings given to those items ) as well as similar decisions made by other users, then use that model to predict items ( or ratings for items ) that the user may have an interest in.
Collaborative filtering methods are based on collecting and analyzing a large amount of information on users ’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users.
One of the most famous examples of Collaborative Filtering is item-to-item collaborative filtering ( people who buy x also buy y ), an algorithm popularized by Amazon. com's recommender system.
Collaborative filtering approaches often suffer from three problems, cold start, scalability, and sparsity.
Collaborative filtering is the method of making automatic predictions ( filtering ) about the interests of a user by collecting taste information from many users ( collaborating ).