Steering via Algorithmic Recommendations

54 Pages Posted: 13 Jan 2020 Last revised: 2 Apr 2020

See all articles by Nan Chen

Nan Chen

National University of Singapore (NUS) - School of Computing

Hsin-Tien Tsai

National University of Singapore (NUS), Department of Economics

Date Written: December 1, 2019


A platform owner can be an intermediator and a seller. Does the dual identity lead to steering incentive? We empirically examine a dual-identity digital platform's use of a product recommendation algorithm called "Frequently Bought Together" (FBT). We analyze unique high-frequency data for over 6 million popular products sold on the platform. First, we document that comparing to non-platform-selling products, platform-selling ones make a similar number of FBT recommendations but receive 73% more. Second, we observe patterns consistent with self-preferencing: controlling for price, sales, and rating, the same product is 8% less likely to be recommended during the platform's temporary absence. Third, we show that recommending platform-selling products may not maximize platform-level sales: "platform to platform" recommendations are 50% less effective than "platform to non-platform" ones. Lastly, focusing on products which both the platform and third parties sell, we document the existence of price competition and a strong positive association between platform dominance and the number of recommendations received.

Keywords: Digital Platform; e-Commerce; Algorithmic Bias; Big Data; Product Recommendation; Platform Design; Vertical Integration

JEL Classification: D22; D43; D83; L11; L43; L81; L86; M21; M31; M38

Suggested Citation

Chen, Nan and Tsai, Hsin-Tien, Steering via Algorithmic Recommendations (December 1, 2019). Available at SSRN: or

Nan Chen (Contact Author)

National University of Singapore (NUS) - School of Computing ( email )

13 Computing Drive
Computing 1
Singapore 117543, 117417


Hsin-Tien Tsai

National University of Singapore (NUS), Department of Economics ( email )


HOME PAGE: http://

Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
PlumX Metrics