Most recommender systems present recommended products in lists to the user. By doing so, much information is lost about the mutual similarity between recommended products. We propose to represent the mutual similarities of the recommended products in a two dimensional space, where similar...

Multidimensional scaling aims at reconstructing dissimilarities between pairs of objects by distances in a low dimensional space. However, in some cases the dissimilarity itself is not known, but the range, or a histogram of the dissimilarities is given. This type of data fall in the wider class...

Multidimensional scaling aims at reconstructing dissimilarities between pairs of objects by distances in a low dimensional space. However, in some cases the dissimilarity itself is unknown, but the range of the dissimilarity is given. Such fuzzy data fall in the wider class of symbolic data...

Er is een steeds sterkere tendens om onderzoeksgegevens te visualiseren in plaats van in tabellen weer te geven. Een belangrijk voordeel van visualisatie is dat de resultaten vaak direct duidelijk en eenvoudig te interpreteren zijn. Zo kan met behulp van meerdimensionele schaling de samenhang...

Perceptual maps are often used in marketing to visually study relations between two or more attributes. However, in many perceptual maps published in the recent literature it remains unclear what is being shown and how the relations between the points in the map can be interpreted or even what a...

Traditionally, recommender systems present recommendations in lists to the user. In content- and knowledge-based recommendation systems these list are often sorted on some notion of similarity with a query, ideal product specification, or sample product. However, a lot of information is lost in...