Business & Information Systems Engineering

, Volume 5, Issue 6, pp 397–408 | Cite as

A Low-Effort Recommendation System with High Accuracy

A New Approach with Ranked Pareto-Fronts
Research Paper

Abstract

In recent studies on recommendation systems, the choice-based conjoint analysis has been suggested as a method for measuring consumer preferences. This approach achieves high recommendation accuracy and does not suffer from the start-up problem because it is also applicable for recommendations for new consumers or of new products. However, this method requires massive consumer input, which causes consumer reluctance. In a simulation study, we demonstrate the high accuracy, but also the high user’s effort for using a utility-based recommendation system using a choice-based conjoint analysis with hierarchical Bayes estimation. In order to reduce the conflict between consumer effort and recommendation accuracy, we develop a novel approach that only shows Pareto-efficient alternatives and ranks them according to the number of dominated attributes. We demonstrate that, in terms of the decision accuracy of the recommended products, the ranked Pareto-front approach performs better than a recommendation system that employs choice-based conjoint analysis. Furthermore, the consumer’s effort is kept low and comparable to that of simple systems that require little consumer input.

Keywords

Recommendation systems Preference measurement Pareto-front Effort Accuracy Simulation 

Supplementary material

12599_2013_295_MOESM1_ESM.pdf (580 kb)
(PDF 2.8 MB)

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Copyright information

© Springer Fachmedien Wiesbaden 2013

Authors and Affiliations

  1. 1.Information Systems and Business Administration Lehrstuhl für Wirtschaftsinformatik und BWLJohannes Gutenberg-Universität MainzMainzGermany
  2. 2.Juniorprofessur für Wirtschaftsinformatik mit Schwerpunkt E-CommerceUniversität PassauPassauGermany

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