Collaborative filtering: special case in predictive analysis

The collaborative filter methods, also called recommender systems, are introduced, which help people in their decisions by deciding for the user which preference he/she is likely to possess on the basis of other users' preferences.

Abstract

The data-flow across the internet has come to a point where the human brain is unable to read, analyze, and conclude the hidden knowledge. One way of helping people in their decisions is the classical query search and filters plugged into various available software in use. Still, the last decision is left to humans where the search for the best alternative is not provided by these queries. The need for a sort of an electronic learner has emerged. Such a type of learner has the main task of deciding for the user which preference (decision) he/she is likely to possess on the basis of other users' preferences (decisions). This field of analysis is called collaborative filtering. Learners have varied in performance and thus in acquiring results on the basis of the different algorithms involved. This article introduces the collaborative filter methods, also called recommender systems. Few publications and research have been elaborated on this topic, but the available studies are encouraging.