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Explaining the user experience of recommender systems

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  • Published: 10 March 2012
  • Volume 22, pages 441–504, (2012)
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Explaining the user experience of recommender systems
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  • Bart P. Knijnenburg1,2,
  • Martijn C. Willemsen2,
  • Zeno Gantner3,
  • Hakan Soncu4 &
  • …
  • Chris Newell5 

  • 26k Accesses

  • 455 Citations

  • 27 Altmetric

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Abstract

Research on recommender systems typically focuses on the accuracy of prediction algorithms. Because accuracy only partially constitutes the user experience of a recommender system, this paper proposes a framework that takes a user-centric approach to recommender system evaluation. The framework links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively). Furthermore, it incorporates the influence of personal and situational characteristics on the user experience. This paper reviews how current literature maps to the framework and identifies several gaps in existing work. Consequently, the framework is validated with four field trials and two controlled experiments and analyzed using Structural Equation Modeling. The results of these studies show that subjective system aspects and experience variables are invaluable in explaining why and how the user experience of recommender systems comes about. In all studies we observe that perceptions of recommendation quality and/or variety are important mediators in predicting the effects of objective system aspects on the three components of user experience: process (e.g. perceived effort, difficulty), system (e.g. perceived system effectiveness) and outcome (e.g. choice satisfaction). Furthermore, we find that these subjective aspects have strong and sometimes interesting behavioral correlates (e.g. reduced browsing indicates higher system effectiveness). They also show several tradeoffs between system aspects and personal and situational characteristics (e.g. the amount of preference feedback users provide is a tradeoff between perceived system usefulness and privacy concerns). These results, as well as the validated framework itself, provide a platform for future research on the user-centric evaluation of recommender systems.

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Acknowledgements

We would like to thank Mark Graus for programming the recommender systems used in EX1 and EX2, Steffen Rendle for implementing the explicit feedback MF algorithm, Niels Reijmer, Yunan Chen and Alfred Kobsa for their comments at several stages of this paper, and Dirk Bollen for allowing us to incorporate the results of his choice overload experiment (EX1) in this paper. We also thank the three anonymous reviewers for their extensive comments on the initial submission. We gratefully acknowledge the funding of our work through the European Commission FP7 project MyMedia (www.mymediaproject.org) under the grant agreement no. 215006. For inquiries please contact info@mymediaproject.org.

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Authors and Affiliations

  1. Department of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA, 92697, USA

    Bart P. Knijnenburg

  2. Human-Technology Interaction Group, School of Innovation Sciences, Eindhoven University of Technology (TU/e), P.O. Box 513, 5600 MB, Eindhoven, The Netherlands

    Bart P. Knijnenburg & Martijn C. Willemsen

  3. Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Marienburger Platz 22, 31141, Hildesheim, Germany

    Zeno Gantner

  4. European Microsoft Innovation Center GmbH, Ritterstrasse 23, 52072, Aachen, Germany

    Hakan Soncu

  5. BBC Research & Development, Centre House, 56 Wood Lane, London, W12 7SB, UK

    Chris Newell

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  4. Hakan Soncu
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Correspondence to Bart P. Knijnenburg.

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Knijnenburg, B.P., Willemsen, M.C., Gantner, Z. et al. Explaining the user experience of recommender systems. User Model User-Adap Inter 22, 441–504 (2012). https://doi.org/10.1007/s11257-011-9118-4

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  • Received: 30 November 2010

  • Accepted: 30 August 2011

  • Published: 10 March 2012

  • Issue Date: October 2012

  • DOI: https://doi.org/10.1007/s11257-011-9118-4

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Keywords

  • Recommender systems
  • Decision support systems
  • User experience
  • User-centric evaluation
  • Decision-making
  • Human-computer interaction
  • User testing
  • Preference elicitation
  • Privacy
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