Adversarial Attacks on Online Learning to Rank with Stochastic Click Models
Abstract: We propose the first study of adversarial attacks on online learning to rank. The goal of the attacker it 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.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Simon_Lacoste-Julien1
Submission Number: 2265
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