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A Low-Effort Recommendation System with High Accuracy

  • Jella Pfeiffer

    ()

  • Michael Scholz

    ()

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    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. Copyright Springer Fachmedien Wiesbaden 2013

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    File URL: http://hdl.handle.net/10.1007/s12599-013-0295-z
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    Article provided by Springer in its journal Business & Information Systems Engineering.

    Volume (Year): 5 (2013)
    Issue (Month): 6 (December)
    Pages: 397-408

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    Handle: RePEc:spr:binfse:v:5:y:2013:i:6:p:397-408
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