Data Poisoning Attacks on Cross-domain Recommendation

Huiyuan Chen, Jing Li

Abstract: Cross-domain recommendation has attracted growing interests given their simplicity and effectiveness. In the cross-domain scenarios, we may improve predictive accuracy in one domain by transferring knowledge from the other, which alleviates the data sparsity issue. However, the relatedness of these domains can be exploited by a malicious party to launch data poisoning attacks. Here we study the vulnerability of cross-domain recommendation under data poisoning attacks. We show that data poisoning attacks can be formulated as a bilevel optimization problem. Our experimental results show that cross-domain system can be compromised under attacks, highlighting the need for countermeasures against data poisoning attacks in cross-domain recommendation.