A Low-Effort Recommendation System with High Accuracy |
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Authors: | Dr Jella Pfeiffer Prof Dr Michael Scholz |
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Institution: | 1. Information Systems and Business Administration Lehrstuhl für Wirtschaftsinformatik und BWL, Johannes Gutenberg-Universit?t Mainz, Jakob-Welder-Weg 9, 55128, Mainz, Germany 2. Juniorprofessur für Wirtschaftsinformatik mit Schwerpunkt E-Commerce, Universit?t Passau, Innstr. 43, 94032, Passau, Germany
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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. |
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