Nuke 'Em Till They Go: Investigating Power User Attacks to Disparage Items in Collaborative Recommenders

Recommender Systems (RSs) can be vulnerable to manipulation by malicious users who successfully bias recommendations for their own benefit or pleasure. These are known as attacks on RSs and are typically used to either promote ("push") or disparage ("nuke") targeted items contained within the recommender's user-item dataset. Our recent work with the Power User Attack (PUA) model, determined that attackers disguised as influential power users can mount successful (from the attacker's viewpoint) push attacks against user-based, item-based, and SVD-based recommenders. However, the success of push attack vectors may not be symmetric for nuke attacks, which target the opposite effect --- reducing the likelihood that target items appear in users' top-N lists. The asymmetry between push and nuke attacks is highlighted when evaluating these attacks using traditional robustness metrics such as Rank and Prediction Shift. This paper examines the PUA attack model in the context of nuke attacks, in order to investigate the differences between push and nuke attack orientations, as well as how they are evaluated. In this work we show that the PUA is able to mount successful nuke attacks against commonly-used recommender algorithms highlighting the "nuke vs. push" asymmetry in the results.