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 ecommerce 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 http://www.vergelijk.nl, and show how the dissimilarity measure can be used to improve 2D product catalog visualizations.

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Keywords Choosing attribute weights, Dissimilarity, Generalized linear models, Knowledge based systems, Map based interfaces, Maximum likelihood, Missing values, Poisson distribution, Poisson equation, Poisson regression, Regression analysis, algorithms
Persistent URL dx.doi.org/10.1145/1454008.1454040, hdl.handle.net/1765/16269
Citation
Kagie, M, Wezel, M.V, & Groenen, P.J.F. (2008). Choosing attribute weights for item dissimilarity using clikstream data with an application to a product catalog map. doi:10.1145/1454008.1454040