Abstract
We run a field experiment to quantify the economic returns to data and informational externalities associated with algorithmic recommendation in the context of online news. Our results show that personalized recommendation can outperform human curation in terms of user engagement, though this crucially depends on the amount of personal data. Limited individual data or breaking news leads the editor to outperform the algorithm. Additional data helps algorithmic performance but decreasing economic returns set in rapidly. Investigating informational externalities highlights that personalized recommendation reduces consumption diversity. Moreover, users associated with lower levels of digital literacy and more extreme political views engage more with algorithmic recommendations.
Original language | English |
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Publication date | 2019 |
Number of pages | 32 |
Publication status | Published - 2019 |
Event | CESifo Area Conference on Global Economy. CESifo Conference Centre - München, Germany Duration: 30 Jan 2004 → 31 Jan 2004 |
Conference
Conference | CESifo Area Conference on Global Economy. CESifo Conference Centre |
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Country/Territory | Germany |
City | München |
Period | 30/01/2004 → 31/01/2004 |