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arXiv · 2305.03995

Attacking Pre-trained Recommendation

Yiqing Wu·Ruobing Xie·Zhao Zhang·Yongchun Zhu·FuZhen Zhuang·Jie Zhou·Yongjun Xu·Qing He
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Abstract

Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained recommendation model for different downstream recommendation tasks. Despite these advancements, the vulnerabilities of classical recommender systems also exist in pre-trained recommendation in a new form, while the security of pre-trained recommendation model is still unexplored, which may threaten its widely practical applications. In this study, we propose a novel framework for backdoor attacking in pre-trained recommendation. We demonstrate the provider of the pre-trained model can easily insert a backdoor in pre-training, thereby increasing the exposure rates of target items to target user groups. Specifically, we design two novel and effective backdoor attacks: basic replacement and prompt-enhanced, under various recommendation pre-training usage scenarios. Experimental results on real-world datasets show that our proposed attack strategies significantly improve the exposure rates of target items to target users by hundreds of times in comparison to the clean model.

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Yiqing Wu, Ruobing Xie, Zhao Zhang, Yongchun Zhu, FuZhen Zhuang, Jie Zhou, Yongjun Xu, Qing He. 2023-05-06. Attacking Pre-trained Recommendation. https://arxiv.org/abs/2305.03995

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