publication . Other literature type . Article . 2010

Manipulation Robustness of Collaborative Filtering

Benjamin Van Roy; Xiang Yan;
  • Published: 01 Nov 2010
  • Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
Abstract
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.
Subjects
free text keywords: enabling technologies (includes artificial intelligence, machine learning, and data mining technologies), probability, stochastic model applications, statistics, nonparametric, Management Science and Operations Research, Strategy and Management, Machine learning, computer.software_genre, computer, Statistical analysis, Nearest neighbour, Collaborative filtering, Expert system, Artificial intelligence, business.industry, business, Robustness (computer science), Computer science
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publication . Other literature type . Article . 2010

Manipulation Robustness of Collaborative Filtering

Benjamin Van Roy; Xiang Yan;