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Online learning to rank (OLTR) is a sequential decision-making problem wherea learning agent selects an ordered list of items and receives feedback throughuser clicks. Although potential attacks against OLTR algorithms may causeserious losses in real-world applications, little is known about adversarialattacks on OLTR. This paper studies attack strategies against multiple variantsof OLTR. Our first result provides an attack strategy against the UCB algorithmon classical stochastic bandits with binary feedback, which solves the keyissues caused by bounded and discrete feedback that previous works can nothandle. Building on this result, we design attack algorithms against UCB-basedOLTR algorithms in position-based and cascade models. Finally, we propose ageneral attack strategy against any algorithm under the general click model.Each attack algorithm manipulates the learning agent into choosing the targetattack item $T-o(T)$ times, incurring a cumulative cost of $o(T)$. Experimentson synthetic and real data further validate the effectiveness of our proposedattack algorithms.

Adversarial Attacks on Online Learning to Rank with Click Feedback