Retrieval coverage limits LLM rerankers in cold-start recommendation; a learned hybrid fusion improves pool quality but LLM reranking often degrades end-to-end performance while simpler rankers exploit the pool.
A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation
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A rank-aware block decomposition for linear and bilinear operations in recommender models (FM, DCNv2, attention, FC) reduces redundant context feature computation to once per request with identity-equivalent results, plus rDCN variant for deeper layers.
Everywhere learning trains AI to meet pointwise loss constraints almost surely, backed by approximate duality theory for generalization and L1 regularization on relaxations.
Modeling recommender systems as control systems shows that time-optimized fairness interventions can improve overall long-term performance rather than merely trading off against utility.
PIMbot introduces an adaptive attack using reward-channel and policy manipulation to disrupt cooperation in multi-robot social dilemma RL, shown effective in Gazebo simulation and on NVIDIA Jetson hardware.
citing papers explorer
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Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation
Retrieval coverage limits LLM rerankers in cold-start recommendation; a learned hybrid fusion improves pool quality but LLM reranking often degrades end-to-end performance while simpler rankers exploit the pool.
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Context Features Are Cheap: Rank-Aware Decomposition for Efficient Feature Interaction in Recommender Systems
A rank-aware block decomposition for linear and bilinear operations in recommender models (FM, DCNv2, attention, FC) reduces redundant context feature computation to once per request with identity-equivalent results, plus rDCN variant for deeper layers.
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Everywhere Learning: Artificial Intelligence with Pointwise Constraints
Everywhere learning trains AI to meet pointwise loss constraints almost surely, backed by approximate duality theory for generalization and L1 regularization on relaxations.
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Recommender Systems as Control Systems
Modeling recommender systems as control systems shows that time-optimized fairness interventions can improve overall long-term performance rather than merely trading off against utility.
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PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning
PIMbot introduces an adaptive attack using reward-channel and policy manipulation to disrupt cooperation in multi-robot social dilemma RL, shown effective in Gazebo simulation and on NVIDIA Jetson hardware.