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 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, we show 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.

Additional Metadata
Keywords dissimilarity measure, map-based interface, multidimensional scaling, nonlinear principal components analysis, recommender systems
JEL Statistical Decision Theory; Operations Research (jel C44), Other Computer Software (jel C88), Information and Product Quality; Standardization and Compatibility (jel L15), Retail and Wholesale Trade; Warehousing; e-Commerce (jel L81), Business Administration and Business Economics; Marketing; Accounting (jel M), Marketing (jel M31)
Publisher Erasmus Research Institute of Management
Persistent URL
Series ERIM Report Series Research in Management
Journal ERIM report series research in management Erasmus Research Institute of Management
Kagie, M, van Wezel, M.C, & Groenen, P.J.F. (2009). Map Based Visualization of Product Catalogs (No. ERS-2009-010-MKT). ERIM report series research in management Erasmus Research Institute of Management. Erasmus Research Institute of Management. Retrieved from