Releases TencentGR-1M and TencentGR-10M datasets with baselines for all-modality generative recommendation in advertising, including weighted evaluation for conversions.
Muthukrishnan, Vishwa Vinay, and Zheng Wen
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Optimal additive baseline IPS asymptotically dominates SNIPS in off-policy evaluation mean squared error.
A2G-DiffRec applies adaptive autoguidance in diffusion recommenders, learning to balance main and weak model outputs via fairness-aware regularization to improve item exposure fairness with only marginal accuracy loss.
Greedy linear models without exploration consistently achieve top-tier performance in over 90% of offline dataset evaluations for linear bandit recommenders, with hyperparameter tuning favoring minimal exploration and exposing biases in these protocols.
citing papers explorer
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Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation
Releases TencentGR-1M and TencentGR-10M datasets with baselines for all-modality generative recommendation in advertising, including weighted evaluation for conversions.
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Additive Control Variates Dominate Self-Normalisation in Off-Policy Evaluation
Optimal additive baseline IPS asymptotically dominates SNIPS in off-policy evaluation mean squared error.
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Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems
A2G-DiffRec applies adaptive autoguidance in diffusion recommenders, learning to balance main and weak model outputs via fairness-aware regularization to improve item exposure fairness with only marginal accuracy loss.
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Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation
Greedy linear models without exploration consistently achieve top-tier performance in over 90% of offline dataset evaluations for linear bandit recommenders, with hyperparameter tuning favoring minimal exploration and exposing biases in these protocols.