Map Based Visualization of Product Catalogs

Abstract

AND KEYWORDS AbstractTraditionally, recommender systems present recommendations in lists to the user. In contentand 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 this way, since two even similar products can differ from the query on a completely different set of product characteristics. When using a two dimensional, that is, a map-based, representation of the recommendations, it is possible to retain this information. In the map we can then position recommendations that are similar to each other in the same area of the map.Both in science and industry an increasing number of two dimensional graphical interfaces have been introduced over the last years. However, some of them lack a sound scientific foundation, while other approaches are not applicable in a recommendation setting. In our chapter, we will describe a framework, which has a solid scientific foundation (using state-of-the-art statistical models) and is specifically designed to work with e-commerce product catalogs. Basis of the framework is the Product Catalog Map interface based on multidimensional scaling. Also, weshow another type of interface based on nonlinear principal components analysis, which provides an easy way in constraining the space based on specific characteristic values. Then, we discuss some advanced issues. Firstly, we discuss how the product catalog interface can be adapted to better fit the users' notion of importance of attributes using click stream analysis.Secondly, we show an user interface that combines recommendation by proposing with the map based approach. Finally, we show how these methods can be applied to a real e-commerce product catalog of MP3-players.Free Keywords map-based interface, multidimensional scaling, nonlinear principal components analysis, recommender systems, dissimilarity measure Availability The ERIM Report Series is distributed through the following platforms:

Explorative Analysis of Recommendations Through Interactive Visualization

“…In particular, Gansner et al [25] use geographic maps for TV show recommendations, Kagie et al [26] use two-dimensional maps for product recommendations in ecommerce, and Verbert et al [27] use clustermaps for talk recommendations at scientific conferences. Specifically concentrating on treemaps, Tintarev and Masthoff [16,28] state that this kind of visualization has not been used for recommender systems even though it may be a valuable choice.…”

A Consensus-Driven Group Recommender System

“…in Ref. . These proposals represent the items in the recommendation set to show similarities between products, e.g., the closer two items are depicted with each other, the more similar they are.…”

3D-Visualisierung zur Eingabe von Präferenzen in Empfehlungssystemen

“…Komplexere Visualisierungen erlauben mitunter auch die Exploration des gesamten Datensatzes, etwa durch Verwendung von Landkarten-Metaphern (Gansner et al 2009). Solche kartenbasierten Darstellungen lassen sich mit verschiedenen Techniken erzeugen (Kagie et al 2010), wobei Multidimensional Scaling besonders hervorzuheben ist. Bei diesem Verfahren werden Ähnlichkeiten zwischen Items derart in einen niedrigdimensionalen Raum abgebildet, dass die Entfernungen in der erzeugten Darstellung die Ähnlichkeiten der Items so genau wie möglich widerspiegeln.…”