The Editor and the Algorithm: Recommendation Technology in Online News

43 Pages Posted: 12 Nov 2019 Last revised: 11 Apr 2023

See all articles by Christian Peukert

Christian Peukert

University of Lausanne - Faculty of Business and Economics (HEC Lausanne)

Ananya Sen

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management

Jörg Claussen

Ludwig Maximilian University of Munich (LMU) - Faculty of Business Administration (Munich School of Management); Copenhagen Business School - Department of Innovation and Organizational Economics

Date Written: January 11, 2021

Abstract

We run a field experiment to study the relative performance of human curation and automated personalized recommendation technology in the context of online news. We build a simple theoretical model that captures the relative efficacy of personalized algorithmic recommendations and curation based on human expertise. We highlight a critical tension between detailed yet potentially narrow information available to the algorithm versus broad (often private) but not scalable information available to the human editor. Empirically we show that, on average, algorithmic recommendations can outperform human curation with respect to clicks, but there is significant heterogeneity in this treatment effect.
The human editor performs relatively better in the absence of sufficient personal data and when there is greater variation in preferences. These results suggest reverting to human curation can mitigate the drawbacks of personalized algorithmic recommendations. Our computations show that the optimal combination of human curation and automated recommendation technology can lead to an increase of up to 13% in clicks. In absolute terms, we provide thresholds for when the estimated gains are larger than our estimate of implementation costs.

Keywords: Field experiment, Algorithmic Recommendation, Value of data, Human Decisions

Suggested Citation

Peukert, Christian and Sen, Ananya and Claussen, Jörg, The Editor and the Algorithm: Recommendation Technology in Online News (January 11, 2021). Available at SSRN: https://ssrn.com/abstract=3479854 or http://dx.doi.org/10.2139/ssrn.3479854

Christian Peukert

University of Lausanne - Faculty of Business and Economics (HEC Lausanne) ( email )

Switzerland

HOME PAGE: http://https://www.christian-peukert.com/

Ananya Sen (Contact Author)

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management ( email )

4800 Forbes Avenue
Pittsburgh, PA Pittsburgh 15213-3890
United States

Jörg Claussen

Ludwig Maximilian University of Munich (LMU) - Faculty of Business Administration (Munich School of Management) ( email )

Kaulbachstr. 45
Munich, DE 80539
Germany

Copenhagen Business School - Department of Innovation and Organizational Economics ( email )

Kilevej 14A
Frederiksberg, 2000
Denmark

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