Collaborative filtering: special case in predictive analysis

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.

Keywords:

• Collaborative filtering
• Recommender systems
• Memory based vs. model based algorithms
• Vector similarity
• Pearson correlation
• Bayesian networks
• Expectation maximization