Attack resistant collaborative filtering

  title={Attack resistant collaborative filtering},
  author={Bhaskar Mehta and Wolfgang Nejdl},
The widespread deployment of recommender systems has lead to user feedback of varying quality. While some users faithfully express their true opinion, many provide noisy ratings which can be detrimental to the quality of the generated recommendations. The presence of noise can violate modeling assumptions and may thus lead to instabilities in estimation and prediction. Even worse, malicious users can deliberately insert attack profiles in an attempt to bias the recommender system to their… CONTINUE READING
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