Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Van Roy, Benjamin - Stanford University | - |
dc.contributor.author | Yan, Xiang - Stanford University | - |
dc.date.accessioned | 2010-01-01T00:04:28Z | - |
dc.date.available | 2010-01-01T00:04:28Z | - |
dc.date.issued | 2009 | - |
dc.identifier.uri | http://hdl.handle.net/2451/29516 | - |
dc.description.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 provide theoretical and empirical results demonstrating that while common nearest neighbor algorithms, which are widely used in commercial systems, can be highly susceptible to manipulation, two classes of collaborative filtering algorithms which we refer to as linear and asymptotically linear are relatively robust. These results provide guidance for the design of future collaborative filtering systems. | en |
dc.relation.ispartofseries | Net Institute Working Paper;09-21 | - |
dc.title | Manipulation Robustness of Collaborative Filtering Systems | en |
Appears in Collections: | NET Institute Working Papers Series |
Files in This Item:
File | Description | Size | Format | |
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Van-Roy_Yan_09-21.pdf | 358.29 kB | Adobe PDF | View/Open |
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