Learning Product Rankings Robust to Fake Users

114 Pages Posted: 20 Oct 2020 Last revised: 31 May 2022

See all articles by Negin Golrezaei

Negin Golrezaei

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Vahideh Manshadi

Yale School of Management

Jon Schneider

Google Inc., New York

Shreyas Sekar

Operations Management Area, Rotman School of Management; Department of Management, University of Toronto Scarborough

Date Written: September 2, 2020

Abstract

In many online platforms, customers' decisions are substantially influenced by product rankings as most customers only examine a few top-ranked products. Concurrently, such platforms also use the same data corresponding to customers' actions to learn how these products must be ranked or ordered. These interactions in the underlying learning process, however, may incentivize sellers to artificially inflate their position by employing fake users, as exemplified by the emergence of click farms. Motivated by such fraudulent behavior, we study the ranking problem of a platform that faces a mixture of real and fake users who are indistinguishable from one another. We first show that existing learning algorithms---that are optimal in the absence of fake users---may converge to highly sub-optimal rankings under manipulation by fake users.

To overcome this deficiency, we develop efficient learning algorithms under two informational environments: in the first setting, the platform is aware of the number of fake users, and in the second setting, it is agnostic to the number of fake users. For both these environments, we prove that our algorithms converge to the optimal ranking, while being robust to the aforementioned fraudulent behavior; we also present worst-case performance guarantees for our methods, and show that they significantly outperform existing algorithms. At a high level, our work employs several novel approaches to guarantee robustness such as: (i) constructing product-ordering graphs that encode the pairwise relationships between products inferred from the customers' actions; and (ii) implementing multiple levels of learning with a judicious amount of bi-directional cross-learning between levels. Overall, our results indicate that online platforms can effectively combat fraudulent users without incurring large costs by designing new learning algorithms that guarantee efficient convergence even when the platform is completely oblivious to the number and identity of the fake users.

Keywords: product ranking, sequential search, online learning, fake users, online platforms

Suggested Citation

Golrezaei, Negin and Manshadi, Vahideh and Schneider, Jon and Sekar, Shreyas, Learning Product Rankings Robust to Fake Users (September 2, 2020). Available at SSRN: https://ssrn.com/abstract=3685465 or http://dx.doi.org/10.2139/ssrn.3685465

Negin Golrezaei

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-416
Cambridge, MA 02142
United States
02141 (Fax)

Vahideh Manshadi

Yale School of Management ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

Jon Schneider

Google Inc., New York ( email )

111 8th Ave
New York, NY 10011
United States

Shreyas Sekar (Contact Author)

Operations Management Area, Rotman School of Management

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

Department of Management, University of Toronto Scarborough

1265 Military Trial
Scarborough, Ontario M1C 1A4
Canada

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