Decision-calibrated conformal uncertainty for pacing uses the support function of the signed policy sensitivity set to achieve smaller uncertainty radii on public datasets.
InProceedings of the 1st Workshop on Deep Learning for Recommender Systems(Boston, MA, USA) (DLRS 2016)
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
DREAM proposes intent-aware tokenization, frozen-model evaluation, and dynamic beams to refine early SID assignments and improve cold-start performance in generative recommenders on Amazon benchmarks.
SAILRec uses dual-side semantic alignment and hierarchical attention steering to improve how LLMs incorporate collaborative embeddings for recommendations, outperforming baselines on MovieLens-1M and Amazon-Book datasets.
citing papers explorer
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Decision-Calibrated Conformal Uncertainty for Pacing Decisions in Streaming Advertising
Decision-calibrated conformal uncertainty for pacing uses the support function of the signed policy sensitivity set to achieve smaller uncertainty radii on public datasets.
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DREAM: Dynamic Refinement of Early Assignment Mappings
DREAM proposes intent-aware tokenization, frozen-model evaluation, and dynamic beams to refine early SID assignments and improve cold-start performance in generative recommenders on Amazon benchmarks.
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SAILRec: Steering LLM Attention to Dual-Side Semantically Aligned Collaborative Embeddings for Recommendation
SAILRec uses dual-side semantic alignment and hierarchical attention steering to improve how LLMs incorporate collaborative embeddings for recommendations, outperforming baselines on MovieLens-1M and Amazon-Book datasets.
- FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation
- ShuffleGate: Scalable Feature Optimization for Recommender Systems via Batch-wise Sensitivity Learning