Abstract
In this paper we propose and examine two different models for customer choices in for instance a wholesale department, given the actual sales. Both customers and products are modeled by points in a k-dimensional real vector space. Two possible strategies are discussed: in one model the customer buys the nearest option from categories of products, in the other he/she buys all products within a certain radius of his/her position. Now we deal with the following problem: given only the sales list, how can we retrieve the relative positions corresponding to customers and products? In particular we are interested in the dimension k of the space: we are looking for low dimensional solutions with a good “fit” to the real sales list. Theoretical complexity of these problems is addressed: they are very hard to solve exactly; special cases are shown to be NP-complete. We use competitive neural network techniques for both artificial and real life data, and report the results.
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Kosters, W.A., van Wezel, M.C. (2002). Competitive Neural Networks for Customer Choice Models. In: Segovia, J., Szczepaniak, P.S., Niedzwiedzinski, M. (eds) E-Commerce and Intelligent Methods. Studies in Fuzziness and Soft Computing, vol 105. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1779-9_4
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DOI: https://doi.org/10.1007/978-3-7908-1779-9_4
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