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Recommending under Threat of Manipulation
Collaborative filtering accomplishes automated personalized content recommendation but by relying on anonymous user feedback is vulnerable to manipulation. Taking measures to improve robustness to outliers seeks to increase resistance against such manipulation. However, this detrimentally impacts recommendation quality even in the absence of such attacks. By taking a competitive game perspectiv...
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ژورنال
عنوان ژورنال: Nature
سال: 1996
ISSN: 0028-0836,1476-4687
DOI: 10.1038/379583a0