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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 algorithmto place the target item on top of the ranking list linear times to timehorizon $T$ with a sublinear attack cost. We propose generalized list poisoningattacks that perturb the ranking list presented to the user. This strategy canefficiently attack any no-regret ranker in general stochastic click models.Furthermore, we propose a click poisoning-based strategy named attack-then-quitthat can efficiently attack two representative OLTR algorithms for stochasticclick models. We theoretically analyze the success and cost upper bound of thetwo proposed methods. Experimental results based on synthetic and real-worlddata further validate the effectiveness and cost-efficiency of the proposedattack strategies.

Adversarial Attacks on Online Learning to Rank with Stochastic Click Models