# Learning Product Rankings Robust to Fake Users

arXiv: https://arxiv.org/abs/2009.05138 · HTML: https://mlsanity.com/paper/2009.05138/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [Learning in Stackelberg Games with Non-myopic Agents](https://mlsanity.com/paper/2208.09407/) (2022)
- [Multi-channel Autobidding with Budget and ROI Constraints](https://mlsanity.com/paper/2302.01523/) (2023)
- [Adversarial Attacks on Online Learning to Rank with Stochastic Click Models](https://mlsanity.com/paper/2305.19218/) (2023)
- [Density-Based Algorithms for Corruption-Robust Contextual Search and Convex Optimization](https://mlsanity.com/paper/2206.07528/) (2022)
- [Learning in Repeated Multi-Unit Pay-As-Bid Auctions](https://mlsanity.com/paper/2307.15193/) (2023)
- [Bridging Adversarial and Nonstationary Multi-armed Bandit](https://mlsanity.com/paper/2201.01628/) (2022)
- [Active Learning for Non-Parametric Choice Models](https://mlsanity.com/paper/2208.03346/) (2022)
- [CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption](https://mlsanity.com/paper/2309.16563/) (2023)
- [Online Ad Procurement in Non-stationary Autobidding Worlds](https://mlsanity.com/paper/2307.05698/) (2023)
- [Misalignment, Learning, and Ranking: Harnessing Users Limited Attention](https://mlsanity.com/paper/2402.14013/) (2024)
- [Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach](https://mlsanity.com/paper/2511.03074/) (2025)
- [Fraud-Proof Revenue Division on Subscription Platforms](https://mlsanity.com/paper/2511.04465/) (2025)

## Abstract (arXiv)

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.