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Comparing Simple Association-Rules and Repeat-Buying Based Recommender Systems in a B2B Environment

  • Andreas Geyer-Schulz
  • Michael Hahsler
  • Anke Thede
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

In this contribution we present a systematic evaluation and comparison of recommender systems based on simple association rules and on repeat-buying theory. Both recommender services are based on the customer purchase histories of a medium-sized B2B-merchant for computer accessories. With the help of product managers an evaluation set for recommendations was generated. With regard to this evaluation set, recommendations produced by both methods are evaluated and several error measures are computed. This provides an empirical test whether frequent item sets or outliers of a stochastic purchase incidence model are suitable concepts for automatically generating recommendations. Furthermore, the loss functions (performance measures) of the two methods are compared and the sensitivity with regard to a misspecification of the model parameters is discussed.

Keywords

Association Rule Recommender System Mining Association Rule Recommender Service Market Basket 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Andreas Geyer-Schulz
    • 1
  • Michael Hahsler
    • 2
  • Anke Thede
    • 1
  1. 1.Schroff-Stiftungslehrstuhl Informationsdienste und elektronische MärkteUniversität Karlsruhe (TH)KarlsruheGermany
  2. 2.Institut für Informationsverarbeitung und InformationswirtschaftWU-WienWienAustria

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