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Recommendation Systems (RS) have become an essential part of many onlineservices. Due to its pivotal role in guiding customers towards purchasing,there is a natural motivation for unscrupulous parties to spoof RS for profits.In this paper, we study the shilling attack: a subsistent and profitable attackwhere an adversarial party injects a number of user profiles to promote ordemote a target item. Conventional shilling attack models are based on simpleheuristics that can be easily detected, or directly adopt adversarial attackmethods without a special design for RS. Moreover, the study on the attackimpact on deep learning based RS is missing in the literature, making theeffects of shilling attack against real RS doubtful. We present a novelAugmented Shilling Attack framework (AUSH) and implement it with the idea ofGenerative Adversarial Network. AUSH is capable of tailoring attacks against RSaccording to budget and complex attack goals, such as targeting a specific usergroup. We experimentally show that the attack impact of AUSH is noticeable on awide range of RS including both classic and modern deep learning based RS,while it is virtually undetectable by the state-of-the-art attack detectionmodel.

Attacking Recommender Systems with Augmented User Profiles