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The impact of consumer preferences on the accuracy of collaborative filtering recommender systems


Despite the omnipresent use of recommender systems in electronic markets, previous research has not analyzed how consumer preferences affect the accuracy of recommender systems. Markets, however, are characterized by a certain structure of consumers’ preferences. Consequently, it is not known in which markets recommender systems perform well. In this paper, we introduce a microeconomic model that allows a systematical analysis of different structures of consumers’ preferences. We develop a model-specific metric to measure the recommendation accuracy. We employ our model in a simulation to evaluate the impact of the structure of the consumers’ preferences on the accuracy of a popular collaborative filtering algorithm. Our study shows that recommendation accuracy is significantly affected by the similarity and number of consumer types and the distribution of consumers. The investigation reveals that in certain markets even random product recommendations outperform the collaborative filtering algorithm.

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  1. 1.

    For example, the MovieLens data set is often used for evaluation (Herlocker et al. 2004).

  2. 2.

    If the most preferred product is positioned at the edge of the differentiation spectrum, the preference spectrum would be smaller. For example, c u  = 0.1 would lead to a length of the preference spectrum of 0.35.

  3. 3.

    The minor gap to an efficiency of 100 % (perfect recommendations) is caused by the random bootstrapping.


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Correspondence to Sebastian Köhler.

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Köhler, S., Wöhner, T. & Peters, R. The impact of consumer preferences on the accuracy of collaborative filtering recommender systems. Electron Markets 26, 369–379 (2016).

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  • Recommender system
  • Consumer preferences
  • Recommendation accuracy
  • Collaborative filtering

JEL Classification

  • M2 Business economics