Differential Evolution in a Recommendation System Based on Collaborative Filtering

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9876)

Abstract

Recommendation systems have become an integral part of e-Commerce websites, as they facilitate the user’s decision-making. To improve the performance of these systems, new techniques are proposed. One of them is the use of the heuristic algorithm, which learn the user’s preferences and provide tailored suggestions. In this article the application of the Differential Evolution algorithm (DE), with a view to creating neighborhood in a Recommendation System, based on the collaborative filtering technique, will be presented. To this end a modified Euclidean metric, which (taking into consideration additional weights found by DE) generates the closest neighborhood for an active user, is used. The results of the experiment are compared with the linear measure of similarity Pearson’s correlation.

Keywords

Recommendation systems Collaborative filtering Differential evolution 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Computer ScienceUnversity of SilesiaSosnowiecPoland

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