Advancing Recommender Systems by mitigating Shilling attacks

arXiv:2404.16177v1 Announce Type: cross
Abstract: Considering the premise that the number of products offered grow in an exponential fashion and the amount of data that a user can assimilate before making a decision is relatively small, recommender systems help in categorizing content according to user preferences. Collaborative filtering is a widely used method for computing recommendations due to its good performance. But, this method makes the system vulnerable to attacks which try to bias the recommendations. These attacks, known as …

arxiv attacks can cs.ai cs.cr cs.ir cs.lg data decision fashion making premise products recommender systems systems