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A Low-Effort Recommendation System with High Accuracy

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  • Jella Pfeiffer

    ()

  • Michael Scholz

    ()

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

Suggested Citation

  • Jella Pfeiffer & Michael Scholz, 2013. "A Low-Effort Recommendation System with High Accuracy," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(6), pages 397-408, December.
  • Handle: RePEc:spr:binfse:v:5:y:2013:i:6:p:397-408
    DOI: 10.1007/s12599-013-0295-z
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    References listed on IDEAS

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    1. Gensler, Sonja & Hinz, Oliver & Skiera, Bernd & Theysohn, Sven, 2012. "Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs," European Journal of Operational Research, Elsevier, vol. 219(2), pages 368-378.
    2. Lohse, Gerald L. & Johnson, Eric J., 1996. "A Comparison of Two Process Tracing Methods for Choice Tasks," Organizational Behavior and Human Decision Processes, Elsevier, vol. 68(1), pages 28-43, October.
    3. Loomes, Graham & Sugden, Robert, 1982. "Regret Theory: An Alternative Theory of Rational Choice under Uncertainty," Economic Journal, Royal Economic Society, vol. 92(368), pages 805-824, December.
    4. Hoyer, Wayne D, 1984. " An Examination of Consumer Decision Making for a Common Repeat Purchase Product," Journal of Consumer Research, Oxford University Press, vol. 11(3), pages 822-829, December.
    5. Michael Yee & Ely Dahan & John R. Hauser & James Orlin, 2007. "Greedoid-Based Noncompensatory Inference," Marketing Science, INFORMS, vol. 26(4), pages 532-549, 07-08.
    6. Eric J. Johnson & John W. Payne, 1985. "Effort and Accuracy in Choice," Management Science, INFORMS, vol. 31(4), pages 395-414, April.
    7. John Butler & Douglas J. Morrice & Peter W. Mullarkey, 2001. "A Multiple Attribute Utility Theory Approach to Ranking and Selection," Management Science, INFORMS, vol. 47(6), pages 800-816, June.
    8. Oliver Hinz & Jochen Eckert, 2010. "The Impact of Search and Recommendation Systems on Sales in Electronic Commerce," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 67-77, April.
    9. repec:eee:joinma:v:25:y:2011:i:2:p:110-122 is not listed on IDEAS
    10. Christian Schlereth & Christine Eckert & Bernd Skiera, 2012. "Using discrete choice experiments to estimate willingness-to-pay intervals," Marketing Letters, Springer, vol. 23(3), pages 761-776, September.
    11. Arnaud De Bruyn & John C. Liechty & Eelko K. R. E. Huizingh & Gary L. Lilien, 2008. "Offering Online Recommendations with Minimum Customer Input Through Conjoint-Based Decision Aids," Marketing Science, INFORMS, vol. 27(3), pages 443-460, 05-06.
    12. Marcel Fritz & Christian Schlereth & Stefan Figge, 2011. "Empirical Evaluation of Fair Use Flat Rate Strategies for Mobile Internet," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 3(5), pages 269-277, October.
    13. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    14. Hey, John D., 1982. "Search for rules for search," Journal of Economic Behavior & Organization, Elsevier, vol. 3(1), pages 65-81, March.
    15. Butler, John C. & Dyer, James S. & Jia, Jianmin & Tomak, Kerem, 2008. "Enabling e-transactions with multi-attribute preference models," European Journal of Operational Research, Elsevier, vol. 186(2), pages 748-765, April.
    16. Stanley F. Biggs & Jean C. Bedard & Brian G. Gaber & Thomas J. Linsmeier, 1985. "The Effects of Task Size and Similarity on the Decision Behavior of Bank Loan Officers," Management Science, INFORMS, vol. 31(8), pages 970-987, August.
    17. Jyrki Wallenius & James S. Dyer & Peter C. Fishburn & Ralph E. Steuer & Stanley Zionts & Kalyanmoy Deb, 2008. "Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead," Management Science, INFORMS, vol. 54(7), pages 1336-1349, July.
    18. Gerald Häubl & Valerie Trifts, 2000. "Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Marketing Science, INFORMS, vol. 19(1), pages 4-21, May.
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    Citations

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    Cited by:

    1. Sebastian Köhler & Thomas Wöhner & Ralf Peters, 2016. "The impact of consumer preferences on the accuracy of collaborative filtering recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(4), pages 369-379, November.
    2. Scholz, Michael & Pfeiffer, Jella & Rothlauf, Franz, 2017. "Using PageRank for non-personalized default rankings in dynamic markets," European Journal of Operational Research, Elsevier, vol. 260(1), pages 388-401.

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