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In practice, users of a Recommender System (RS) fall into a few clustersbased on their preferences. In this work, we conduct a systematic study onuser-cluster targeted data poisoning attacks on Matrix Factorisation (MF) basedRS, where an adversary injects fake users with falsely crafted user-itemfeedback to promote an item to a specific user cluster. We analyse how user anditem feature matrices change after data poisoning attacks and identify thefactors that influence the effectiveness of the attack on these featurematrices. We demonstrate that the adversary can easily target specific userclusters with minimal effort and that some items are more susceptible toattacks than others. Our theoretical analysis has been validated by theexperimental results obtained from two real-world datasets. Our observationsfrom the study could serve as a motivating point to design a more robust RS.

Evaluating Impact of User-Cluster Targeted Attacks in Matrix Factorisation Recommenders