Recommender systems play a pivotal role in e-commerce, social media, and content streaming platforms by personalizing user experiences and driving engagement. While enhancing the performance of these systems is crucial, ensuring their robustness is equally important to safeguard against security threats. Despite extensive research addressing adversarial and shilling attacks on recommender systems, backdoor attacks remain underexplored. This paper introduces BadEmbNets, an innovative framework for executing backdoor attacks on visually-aware recommender systems. Our experiments demonstrate that an attacker can effectively elevate the rank of compromised items by embedding triggers in their images without affecting the performance of benign items. This work motivates further research into backdoor attacks against recommender systems.
Nguyen et al. (Thu,) studied this question.