A new Recommendation Model for the User Clustering-Based Recommendation System

Aurimas Rapečka, Gintautas Dzemyda


The aim of this paper is to create a new recommendation model, that would evaluate peculiarities of user groups, and to examine experimentally the efficiency of user clustering in order to improve the recommendations. The research has disclosed dependencies of the efficiency of recommendations on the number of clusters. The new recommendation model is proposed here for the user clustering-based recommendation system.

DOI: http://dx.doi.org/10.5755/j01.itc.44.1.5931


recommendation systems, user clustering, user filtering algorithms

Full Text: PDF

Print ISSN: 1392-124X 
Online ISSN: 2335-884X