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Assessing-Perceptual-and-Recommendation-Mutation-of-Adversarially-Poisoned-Visual-Recommenders

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In this work, we provide 24 combinations of attack/defense strategies, and visual-based recommenders to 1) access performance alteration on recommendation and 2) empirically verify the effect on final users through offline visual metrics.

adversarial-attacksdeep-learninghuman-in-the-looprecommender-systemtrustworthiness
Creat：2021-02-12T00:28:56
Update：2021-02-12T00:31:00
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