Hierarchical graph maps for visualization of collaborative recommender systems

Abstract

In this paper we provide a method that allows the visualization of similarity relationships present between items of collaborative filtering recommender systems, as well as the relative importance of each of these. The objective is to offer visual representations of the recommender system’s set of items and of their relationships; these graphs show us where the most representative information can be found and which items are rated in a more similar way by the recommender system’s community of users. The visual representations achieved take the shape of phylogenetic trees, displaying the numerical similarity and the reliability between each pair of items considered to be similar. As a case study we provide the results obtained using the public database Movielens 1M, which contains 3900 movies.