Manipulation Robustness of Collaborative Filtering

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 commer...

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Bibliographic Details
Published in:Management science 2010-11, Vol.56 (11), p.1911-1929
Main Authors: Van Roy, Benjamin, Yan, Xiang
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
and data mining technologies
Applied sciences
Artificial intelligence
Collaboration
Collaborative filtering
Commerce
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Data mining
Data processing. List processing. Character string processing
Datasets
Decision analysis
Decision making
enabling technologies (includes artificial intelligence
enabling technologies (includes artificial intelligence, machine learning, and data mining technologies)
Exact sciences and technology
Filtering systems
Internet
machine learning
Management
Management science
Mathematical robustness
Mathematical vectors
Memory organisation. Data processing
nonparametric
Probability
Purchasing
Ratings & rankings
Recommendations
Recommender systems
Sales
Scalars
Social influence
Software
statistics
stochastic model applications
Stochastic models
Studies
Training
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Description
Summary: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.
ISSN:0025-1909
1526-5501
DOI:10.1287/mnsc.1100.1232