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