PONA integrates the LCPI estimator for new action selection with the DR estimator for existing actions to optimize policies in offline contextual bandits with evolving action spaces.
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4 Pith papers cite this work. Polarity classification is still indexing.
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APG4RecSim automatically generates realistic user profiles for LLM-based recommendation simulations, outperforming manual baselines by up to 7% in nDCG@10 and 8% in JSD on three benchmark datasets.
DPAA mitigates popularity bias in GNN-based collaborative filtering by integrating adaptive embedding-aware interaction weighting stabilized from pre-trained embeddings and layer-wise amplification of higher-order neighborhoods, outperforming prior debiasing methods on real and semi-synthetic data.
OPLS is a new off-policy learning method for contextual bandits with limited supply that outperforms conventional greedy approaches by prioritizing items with relatively higher expected rewards compared to other users.
citing papers explorer
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Task-Aware Automated User Profile Generation for Recommendation Simulation Using Large Language Models
APG4RecSim automatically generates realistic user profiles for LLM-based recommendation simulations, outperforming manual baselines by up to 7% in nDCG@10 and 8% in JSD on three benchmark datasets.
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Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering
DPAA mitigates popularity bias in GNN-based collaborative filtering by integrating adaptive embedding-aware interaction weighting stabilized from pre-trained embeddings and layer-wise amplification of higher-order neighborhoods, outperforming prior debiasing methods on real and semi-synthetic data.