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

Abstract

The aim of this paper is to create a new recommendation method that would evaluate the peculiarities of user groups, and to examine experimentally the efficiency of user clustering in order to improve the recommendations. To achieve this goal, we have analysed recommendation systems (RS), their components, operating principles and data, used for accuracy evaluation. The proposed method is based on user clustering; therefore, clustering-based RS are reviewed. Finally, the proposed method is presented and tested with the most appropriate data set of all that discussed in the overview. The research has disclosed dependencies of the efficiency of recommendations on the number of clusters. The experimental results have shown that the proposed method can be applied to high density databases and the results of recommendations are better than those of traditional methods.

Session Recommendation Based on Edge Information Clustering

“…In addition, group recommendation is also a common recommendation method, where group users usually have the same or similar preferences [19]. Therefore, edge information is generally used to cluster users in group recommendations [20]. Group recommendations can balance the needs of multiple users, or an appropriate model can be built for a group of users to improve the quality of recommendations [21].…”

A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

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