Adversarial Attacks on Online Learning to Rank with Stochastic Click
  Models
Zichen Wang Rishab Balasubramanian Hui Yuan Chenyu Song Mengdi Wang Huazheng Wang
We propose the first study of adversarial attacks on online learning to rank.
The goal of the adversary is to misguide the online learning to rank algorithm
to place the target item on top of the ranking list linear times to time
horizon $T$ with a sublinear attack cost. We propose generalized list poisoning
attacks that perturb the ranking list presented to the user. This strategy can
efficiently attack any no-regret ranker in general stochastic click models.
Furthermore, we propose a click poisoning-based strategy named attack-then-quit
that can efficiently attack two representative OLTR algorithms for stochastic
click models. We theoretically analyze the success and cost upper bound of the
two proposed methods. Experimental results based on synthetic and real-world
data further validate the effectiveness and cost-efficiency of the proposed
attack strategies.