When power users attack: assessing impacts in collaborative recommender systems

Abstract

Power users, in a Collaborative Filtering (CF) Recommender System (RS) context, are those who can exert considerable influence over the recommendation outcomes presented to other users. RS operators encourage the existence of power user communities and leverage them to help fellow users make informed purchase decisions. Thus, RS research in this area has focused on power user identification and utilization to address challenges such as rating sparsity for new items or users. But, as ever, there remains the potential for corruption of power. Alongside accuracy and efficiency measures, RS robustness to manipulation or 'attack' has been studied using injection of false user profiles. Our research is investigating the impact on RS predictions and top-N recommendation lists when simulated power users provide biased ratings for new items. In this study, we introduce the notion of a 'Power User Attack' for RS robustness analysis, as well as a novel use of social networking degree centrality concepts for identifying RS power users. Initial results show that power users identified using in-degree centrality, compared to other techniques, can be more influential as reflected by accuracy and robustness impacts before and after power user attacks.