Accuracy and robustness impacts of power user attacks on collaborative recommender systems

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 selection and utilization to address challenges such as rating sparsity for new items or users. But, as ever, there remains the potential for corruption by power users who provide biased ratings. And because of the influence that power users wield, biased ratings they provide can have significant impacts on RS accuracy and robustness. In order to better understand this problem and develop solution strategies, our research is investigating the impact on RS predictions and top-N recommendation lists when power users provide biased ratings. The open areas of research we have explored are analyzing and evaluating power user selection techniques, statistically characterizing power users in order to create attack profiles, mounting power user attacks on new items, and using accuracy and robustness metrics to evaluate power user attacks. In the future, we plan to extend our initial research in power user selection, characterization, and evaluation, as well as generate attack profiles based on power user characteristics, mount power user attacks on user-based, item-based, and SVD-based CF systems, evaluate power user attacks, and generalize our work across different domains.