Recommender systems and content discovery platforms are active information filtering systems that attempt to present to the user information items (film, television, music, books, news, web pages) the user is interested in.
This filtering operation is also present in schools and universities where there is a selection of information to provide assistance based on academic criteria to customers of this service, the students.
With this problem, it began to devise new filtering with which we can get the information required for each specific topic to easily and efficiently.
These filters are also used to organize and structure information in a correct and understandable way, in addition to group messages on the mail addressed.
To carry out the learning process, part of the information has to be pre-filtered, which means there are positive and negative examples which we named training data, which can be generated by experts, or via feedback from ordinary users.
Nowadays, there are numerous techniques to develop information filters, some of these reach error rates lower than 10% in various experiments.
[citation needed] Among these techniques there are decision trees, support vector machines, neural networks, Bayesian networks, linear discriminants, logistic regression, etc.. At present, these techniques are used in different applications, not only in the web context, but in thematic issues as varied as voice recognition, classification of telescopic astronomy or evaluation of financial risk.