Learning Product Rankings Robust to Fake Users

In many online platforms, customers’ decisions are substantially influenced by product rankings as most customers only examine a few top-ranked products. This induces a race for visibility among sellers, who may be incentivized 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 problem of learning product rankings when a platform 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 suboptimal rankings under manipulation. To overcome this deficiency, we develop efficient learning algorithms under two informational settings: when the platform is aware of the number of fake users and when it is agnostic to this number. For both these settings, we prove that our algorithms converge to the optimal ranking, yet being robust to the aforementioned fraudulent behavior; we also present worst case performance guarantees for our methods and show that they outperform existing algorithms. At a high level, our work employs several novel approaches to guarantee robustness, such as (i) encoding product relationships using graphs and (ii) implementing multiple levels of learning as well as judicious cross-learning. Overall, our results indicate that online platforms can effectively combat fraudulent users even when they are completely oblivious to the number and identity of the fake users.