An Empirical Comparison of Dissimilarity Measures for Recommender Systems

Martijn Kagie (), M.C. van Wezel and Patrick Groenen ()

No ERS-2009-023-MKT Revision_Date: 2009-07-29, Research Paper from Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

Abstract: Many content-based recommendation approaches are based on a dissimilarity measure based on the product attributes. In this paper, we evaluate four dissimilarity measures for product recommendation using an online survey. In this survey, we asked users to specify which products they considered to be relevant recommendations given a reference product. We used microwave ovens as product category. Based on these responses, we create a relative relevance matrix we use to evaluate the dissimilarity measures with. Also, we use this matrix to estimate weights to be used in the dissimilarity measures. In this way, we evaluate four dissimilarity measures: the Euclidean Distance, the Hamming Distance, the Heterogeneous Euclidean-Overlap Metric, and the Adapted Gower Coefficient. The evaluation shows that these weights improve recommendation performance. Furthermore, the experiments indicate that when recommending a single product, the Heterogeneous Euclidean-Overlap Metric should be used and when recommending more than one product the Adapted Gower Coefficient is the best alternative. Finally, we compare these dissimilarity measures with a collaborative method and show that this method performs worse than the dissimilarity based approaches.

Keywords: dissimilarity; case-based recommendation; evaluation; weight estimation (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-exp
Date: 2009-05-07

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