Product Recommendations and Market Concentration

Research Seminars

The paper presented in this ZEW Research Seminar explores the impact of algorithms that use personal data to predict tastes and make recommendations, on consumption choices and in particular on the level of market concentration. The authors model consumer preferences in a flexible way that allows for varying degrees of vertical and horizontal product differentiation. Like previous analyses, they find that recommender systems produce a significant degree of product market concentration. Contrary to the conventional wisdom however, the authors show that such increase in market concentration is not due to the “feedback loop” created by the endogeneity of the data. They discuss the implications of their findings for the intensity of competition and competition policy.


  • Prof. Emilio Calvano Ph.D.

    Prof. Emilio Calvano Ph.D. // Alma Mater Studiorum – Università di Bologna, Italy

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