Manipulation Robustness of Collaborative Filtering
研究了协同过滤系统易受恶意操纵的问题，发现近邻算法极易被操纵，并提出了相对鲁棒的新算法。
A collaborative filtering system recommends to users products that similar users like. Collaborative filtering systems influence purchase decisions and hence have become targets of manipulation by unscrupulous vendors. We demonstrate that nearest neighbors algorithms, which are widely used in commercial systems, are highly susceptible to manipulation and introduce new collaborative filtering algorithms that are relatively robust.