Decision-calibrated conformal uncertainty for pacing uses the support function of the signed policy sensitivity set to achieve smaller uncertainty radii on public datasets.
Deep interest network for click-through rate prediction
9 Pith papers cite this work. Polarity classification is still indexing.
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RankElastor mitigates embedding collapse via spectrum-robust token mixing and GLU-based P-FFNs, yielding better performance and scaling on industrial recommendation datasets.
FLUID introduces LUCID semantic codes from a multimodal encoder to retire item IDs in livestreaming rankers, with staged warmup yielding online gains of +0.55% watch duration and +2.05% cold-start views.
A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on index nodes.
DeGRe decouples offline exploration via a lookahead evaluator using beam search and cumulative regression to distill dense supervision into an online generator that approximates optimal reranking sequences with greedy decoding.
TwiSTAR learns to switch between fast SID retrieval and slow rationale-generating reasoning in generative recommendation, yielding better accuracy-latency trade-offs on three datasets.
PRISM improves e-commerce search robustness by modeling preference-relevance interactions via preference rectification, LLM-driven semantic anchoring with prototypes, and preference-conditioned evidence routing.
IID-Nav enables progressive retrieval in large-scale recommenders by treating it as iterative goal-driven graph traversal with recursive state evolution supporting unlimited depth without rising inference cost.
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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Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation
RankElastor mitigates embedding collapse via spectrum-robust token mixing and GLU-based P-FFNs, yielding better performance and scaling on industrial recommendation datasets.
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FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation
FLUID introduces LUCID semantic codes from a multimodal encoder to retire item IDs in livestreaming rankers, with staged warmup yielding online gains of +0.55% watch duration and +2.05% cold-start views.
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Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation
A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on index nodes.
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DeGRe: Dense-supervised Generative Reranking for Recommendation
DeGRe decouples offline exploration via a lookahead evaluator using beam search and cumulative regression to distill dense supervision into an online generator that approximates optimal reranking sequences with greedy decoding.
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TwiSTAR:Think Fast, Think Slow, Then Act,Generative Recommendation with Adaptive Reasoning
TwiSTAR learns to switch between fast SID retrieval and slow rationale-generating reasoning in generative recommendation, yielding better accuracy-latency trade-offs on three datasets.
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PRISM: Refracting the Entangled User Behavior Space for E-Commerce Search
PRISM improves e-commerce search robustness by modeling preference-relevance interactions via preference rectification, LLM-driven semantic anchoring with prototypes, and preference-conditioned evidence routing.
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From Extraction to Navigation: Progressive Retrieval with Indirectly Infinite Depth
IID-Nav enables progressive retrieval in large-scale recommenders by treating it as iterative goal-driven graph traversal with recursive state evolution supporting unlimited depth without rising inference cost.
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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.