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Evaluating Recommender Systems with User Experiments

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Abstract

Proper evaluation of the user experience of recommender systems requires conducting user experiments. This chapter is a guideline for students and researchers aspiring to conduct user experiments with their recommender systems. It first covers the theory of user-centric evaluation of recommender systems, and gives an overview of recommender system aspects to evaluate. It then provides a detailed practical description of how to conduct user experiments, covering the following topics: formulating hypotheses, sampling participants, creating experimental manipulations, measuring subjective constructs with questionnaires, and statistically evaluating the results.

Keywords

  • Confirmatory Factor Analysis
  • Recommender System
  • Preference Elicitation
  • Average Variance Extract
  • Situational Characteristic

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 author contributed to this chapter while he was at the University of California, Irvine.

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Fig. 9.1
Fig. 9.2

Notes

  1. 1.

    We use the term “user experiment” to denote the use of experimental conditions and formal measurement as a means of testing theories about users interacting with recommender systems. This as opposed to “user studies”, which are typically smaller observational studies used to iteratively improve the usability of a recommender system.

  2. 2.

    See [45] for a taxonomy of different types of theory.

  3. 3.

    Like Hassenzahl [46, 47], our framework describes the formation of experiences during technology use rather than the longer-term phenomenon of technology acceptance, but it extends this model to behavioral consequences using attitude-behavior theories [24, 37] (a theoretical structure that is prominent in technology acceptance models [26, 116]).

  4. 4.

    The paths from Personal and Situation Characteristics to Subjective System Aspects were added to the original framework (as presented in [67]) based on insights from various experiments with the framework.

  5. 5.

    In some cases PCs and SCs can be inferred from user behavior, e.g. observing the click-stream can tell us the market segment a user belongs to [44]. SCs can also be manipulated, e.g. by priming users to approach the recommender with either a concrete or abstract mindset [71, 120].

  6. 6.

    Mechanical Turk is currently only available for researchers in the United States, but various alternatives for non-US researchers exist.

  7. 7.

    http://www.statmodel.com/.

  8. 8.

    http://lavaan.ugent.be/.

  9. 9.

    Or, multiple measurement scales for the different constructs (e.g. system satisfaction, ease of use, and recommendation quality), each measured with multiple items.

  10. 10.

    MPlus and Lavaan use a different parameterization by default by fixing the loading of the first item to 1. We free up these loadings by including an asterisk after (MPlus) or NA* before (Lavaan) the first item of each factor. This alternative solution conveniently standardizes the factor scores.

  11. 11.

    Moreover, even if you are more or less certain about the factor structure of a CFA model, it pays to consult the modification indices of the model. The use of modification indices and CFA goes beyond the current chapter, but is thoroughly explained in Kline’s [59] practical primer on Structural Equation Models.

  12. 12.

    An important property of the “interval” data type is that differences between values are comparable. This is for instance not true for a rating score: the difference between 1 and 2 stars is not necessarily the same as the difference between 3 and 4 stars (cf. [74]).

  13. 13.

    Here we do not discuss the interaction effect between inspectability and control. This interaction can be tested by multiplying their dummies, creating cgraphitem and cgraphfriend. These dummies represent the additional effect of item- and friend-control in the graph condition (and likewise, the additional effect of the graph in the item- and friend-control conditions).

  14. 14.

    By design, experimental manipulations can only be independent variables (i.e. they never have incoming arrows), so they always start the causal chain.

  15. 15.

    Like in CFA, more exploratory model efforts can be assisted by the use of modification indices. Please consult [59] for examples.

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Knijnenburg, B.P., Willemsen, M.C. (2015). Evaluating Recommender Systems with User Experiments. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_9

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