Manipulation Robustness of Collaborative Filtering

• 成果类型: Article
• 署名作者: Van Roy, Benjamin; Yan, Xiang
• 署名单位: Stanford University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.1100.1232
• 发表日期: 2010
• 页码: 1911-1929
• 关键词: enabling technologies (includes artificial intelligence machine learning and data mining technologies); probability; stochastic model applications; statistics; Nonparametric

摘要

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.