A Low-Effort Recommendation System with High Accuracy

Jella Pfeiffer () and Michael Scholz ()

Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, 2013, vol. 5, issue 6, 397-408

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. Copyright Springer Fachmedien Wiesbaden 2013

Keywords: Recommendation systems; Preference measurement; Pareto-front; Effort; Accuracy; Simulation (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s12599-013-0295-z

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