Human Decision Making and Recommender Systems

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.

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Notes

There is no crisp distinction in English between “choosing” and “deciding”. We will mostly use the former term, since “decision making” is often associated with complex problems requiring deep thought and analysis, whereas recommender systems are more commonly used in connection with smaller, less complex problems.

To avoid clumsy formulations like “him- or herself” when using personal pronouns in a generic way, we will alternate between the masculine and feminine forms on an example-by-example basis.

Much more detail on the Aspect and Arcade models will be found in the book-length monograph by Jameson et al. [39].

More discussion of the distinction between the contrasting goals of persuasion and choice support is given in [39, Sect. 1.2].

Aspect is an acronym formed from the first letters of the six patterns.

In the terminology of the Arcade model (Sect. 18.3 below), this strategy is called Evaluate on Behalf of the Chooser. As we will see in that section, recommender systems typically also support choice with applications of other strategies that are not specifically associated with recommendation technology.

This type of awareness is often absent, as when the recommender system adapts the order in which a list of options is presented to the user without announcing the fact that it is doing so.

This type of advice is often given implicitly, in that the system provides support for one procedure but not for others (e.g., by reminding the chooser of her past experience but providing no information about what other people choose).

A thorough discussion of credibility in human and artificial advice giving can be found in Chap. 20.

In the recommender systems field, relatively few attempts have been made to measure and model preferences in such a relative way, for example by using interfaces in which users rank a set of items; but see, for instance, the work of Boutilier and colleagues (e.g., [54]).

See [2, 77] and [35] for discussions of rating noise.

The most widely recounted—and most often overinterpreted—example of choice overload is the “jam study” of Iyengar and Lepper [38].

This sort of advice might be given selectively only to choosers who had been identified as likely maximizers with the help of one of the relevant testing scales [64, 80].

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Acknowledgements

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 2011 Footnote 14 and ACM RecSys 2011, Footnote 15 2012, Footnote 16 2013, Footnote 17 and 2014 Footnote 18 ; a special issue of the ACM Transactions on Interactive Intelligent Systems [14]; and a workshop in September of 2014 at the University of Bolzano. Footnote 19

Author information

Authors and Affiliations

  1. DFKI, German Research Center for Artificial Intelligence, Saarbrücken, Germany Anthony Jameson
  2. Eindhoven University of Technology, Eindhoven, The Netherlands Martijn C. Willemsen
  3. University of Graz, Graz, Austria Alexander Felfernig
  4. Department of Computer Science, University of Bari “Aldo Moro”, Bari, Italy Marco de Gemmis, Pasquale Lops & Giovanni Semeraro
  5. Hong Kong Baptist University, Hong Kong, China Li Chen
  1. Anthony Jameson