Human Decision Making and Recommender Systems

  • Anthony Jameson
  • Martijn C. Willemsen
  • Alexander Felfernig
  • Marco de Gemmis
  • Pasquale Lops
  • Giovanni Semeraro
  • Li Chen


If we assume that an important function of recommender systems is to help people make better choices, it follows that people who design and study recommender systems ought to have a good understanding of how people make choices and how human choice can be supported. This chapter starts with a compact synthesis of research on the various ways in which people make choices in everyday life, in terms of six choice patterns; we explain for each pattern how recommender systems can support its application, both in familiar ways and in ways that have not been explored so far. Similarly, we distinguish six high-level strategies for supporting choice, noting that one strategy is directly supported by recommendation technology but that the others can also be applied fruitfully in recommender systems. We then illustrate how this conceptual framework can be used to shed new light on several fundamental questions that arise in recommender systems research: In what ways can explanations of recommendations support choice processes? What are we referring to when we speak of a person’s “preferences”? What goes on in people’s heads when they rate an item? What is “choice overload”, and how can recommender systems help prevent it? How can recommender systems help choosers to engage in trial and error? What subtle influences on choice can arise when people choose among a small number of options; and how can a recommender system take them into account? One general contribution of the chapter is to generate new ideas about how recommendation technology can be deployed in support of human choice, often in conjunction with other strategies and technologies.


Recommender System Preference Model Choice Situation Choice Process Choice Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The preparation of this chapter benefited from a series of initiatives that have taken place since 2011 under titles similar to the title of this chapter: workshops at the conferences UMAP 201114 and ACM RecSys 2011,15 2012,16 2013,17 and 201418; a special issue of the ACM Transactions on Interactive Intelligent Systems [14]; and a workshop in September of 2014 at the University of Bolzano.19


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Anthony Jameson
    • 1
  • Martijn C. Willemsen
    • 2
  • Alexander Felfernig
    • 3
  • Marco de Gemmis
    • 4
  • Pasquale Lops
    • 4
  • Giovanni Semeraro
    • 4
  • Li Chen
    • 5
  1. 1.DFKI, German Research Center for Artificial IntelligenceSaarbrückenGermany
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.University of GrazGrazAustria
  4. 4.Department of Computer ScienceUniversity of Bari “Aldo Moro”BariItaly
  5. 5.Hong Kong Baptist UniversityHong KongChina

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