Robustness and Accuracy Tradeoffs for Recommender Systems Under Attack

Recommender systems assist users in the daunting task of sifting through large amounts of data in order to select relevant information or items. Common examples include consumer products and services, such as for songs, books, articles, etc. Unfortunately, such systems may be subject to attack by malicious users who want to manipulate the system’s recommendations to suit their needs: to promote their own (or demote a competitor’s) product/service, or to cause disruption in the recommender system. Attacks can cause the recommender system to become unreliable and untrustworthy, resulting in user dissatisfaction. Developers already face tradeoffs in system efficiency and accuracy, and designing for robustness adds an additional dimension for consideration. In this paper, we show how the underlying implementation choices for item-based and user-based Collaborative Filtering recommender systems can affect the accuracy and robustness of recommender systems. We also show how accuracy and robustness can change over a system’s lifetime by analyzing a set of temporal snapshots from system usage over time. Results provide insight into some of the tradeoffs between robustness and accuracy that operators may need to consider in development and evaluation.