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Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders. (arXiv:2207.11237v1 [cs.IR])
July 25, 2022, 1:20 a.m. | Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang
cs.CR updates on arXiv.org arxiv.org
While sequential recommender systems achieve significant improvements on
capturing user dynamics, we argue that sequential recommenders are vulnerable
against substitution-based profile pollution attacks. To demonstrate our
hypothesis, we propose a substitution-based adversarial attack algorithm, which
modifies the input sequence by selecting certain vulnerable elements and
substituting them with adversarial items. In both untargeted and targeted
attack scenarios, we observe significant performance deterioration using the
proposed profile pollution algorithm. Motivated by such observations, we design
an efficient adversarial defense method called …
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