In content- and knowledge-based recommender systems often a measure of (dis)similarity between items is used. Frequently, this measure is based on the attributes of the items. However, which attributes are important for the users of the system remains an important question to answer. In this paper, we present an approach to determine attribute weights in a dissimilarity measure using clickstream data of an e-commerce website. Counted is how many times products are sold and based on this a Poisson regression model is estimated. Estimates of this model are then used to determine the attribute weights in the dissimilarity measure. We show an application of this approach on a product catalog of MP3 players provided by Compare Group, owner of the Dutch price comparison site, and show how the dissimilarity measure can be used to improve 2D product catalog visualizations.

Additional Metadata
Keywords attribute weights, clickstream data, comparison, dissimilarity measure
JEL Statistical Decision Theory; Operations Research (jel C44), Forecasting and Other Model Applications (jel C53), Information, Knowledge, and Uncertainty (jel D8), 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. (2008). Choosing Attribute Weights for Item Dissimilarity using Clikstream Data with an Application to a Product Catalog Map (No. ERS-2008-024-MKT). ERIM report series research in management Erasmus Research Institute of Management. Erasmus Research Institute of Management. Retrieved from