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Found 2 papers, 2 papers with code
Uplift Modeling for Target User Attacks on Recommender Systems
UBA estimates the treatment effect on each target user and optimizes the allocation of fake user budgets to maximize the attack performance.
RecAD: Towards A Unified Library for Recommender Attack and Defense
Despite significant research progress in recommender attack and defense, there is a lack of a widely-recognized benchmarking standard in the field, leading to unfair performance comparison and limited credibility of experiments.
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