A structure-aware RL fairness attack with joint item and gender selection policies is introduced and shown effective on four recommender models across two datasets.
The movielens datasets: History and context
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cs.IR 2years
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UNVERDICTED 2representative citing papers
CoARS enables co-evolving recommender and user agents by using interaction-derived rewards and self-distilled credit assignment to internalize multi-turn feedback into model parameters, outperforming prior agentic baselines.
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Fairness Attacks on Recommender Systems
A structure-aware RL fairness attack with joint item and gender selection policies is introduced and shown effective on four recommender models across two datasets.
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Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems
CoARS enables co-evolving recommender and user agents by using interaction-derived rewards and self-distilled credit assignment to internalize multi-turn feedback into model parameters, outperforming prior agentic baselines.