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Consumer decision making in knowledge-based recommendation

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Abstract

In contrast to customers of bricks and mortar stores, users of online selling environments are not supported by human sales experts. In such situations recommender applications help to identify the products and/or services that fit the user’s wishes and needs. In order to successfully apply recommendation technologies we have to develop an in-depth understanding of decision strategies of users. These decision strategies are explained in different models of human decision making. In this paper we provide an overview of selected models and discuss their importance for recommender system development. Furthermore, we provide an outlook on future research issues.

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Notes

  1. Note that we use the robot product domain in the following examples.

  2. www.pandora.com

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Acknowledgements

The presented work has been developed within the scope of the research project XPLAIN-IT (funded by the Privatstiftung Kärnter Sparkasse).

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Authors and Affiliations

  1. Institute for Software Technology, Graz University of Technology, Inffeldgasse 16b, 8010, Graz, Austria

    Monika Mandl, Alexander Felfernig & Monika Schubert

  2. Applied Informatics, University of Klagenfurt, Universitätsstrasse 65-67, 9020, Klagenfurt, Austria

    Erich Teppan

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  1. Monika Mandl
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  2. Alexander Felfernig
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  4. Monika Schubert
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Correspondence to Monika Mandl.

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Mandl, M., Felfernig, A., Teppan, E. et al. Consumer decision making in knowledge-based recommendation. J Intell Inf Syst 37, 1–22 (2011). https://doi.org/10.1007/s10844-010-0134-3

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