GLAN replaces CQL bootstrapping with Decision Transformer sequence modeling for PLPM, using global inter-day (L-RTG) and local session (HRM) modules to achieve +0.158% DAU and +0.108% LT gains in Kuaishou online tests.
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4 Pith papers cite this work, alongside 15 external citations. Polarity classification is still indexing.
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Pith papers citing it
15
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representative citing papers
RGCD-Rep distills cross-domain reasoning from a frozen MLLM teacher and learns decomposed transferable item representations via two-stage training, yielding gains in offline experiments and production A/B tests on a live streaming platform.
DMICF models interactions from user- and item-centric perspectives with a macro-micro prototype-aware variational encoder and dimension-wise intent alignment to improve collaborative filtering.
citing papers explorer
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From Bootstrapping to Sequence Modeling: A Unified Generative Framework for Personalized Landing-Page Modeling
GLAN replaces CQL bootstrapping with Decision Transformer sequence modeling for PLPM, using global inter-day (L-RTG) and local session (HRM) modules to achieve +0.158% DAU and +0.108% LT gains in Kuaishou online tests.
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Bridging Short Videos and Live Streams: Reasoning-Guided Multimodal LLMs for Cross-Domain Representation Learning
RGCD-Rep distills cross-domain reasoning from a frozen MLLM teacher and learns decomposed transferable item representations via two-stage training, yielding gains in offline experiments and production A/B tests on a live streaming platform.
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Dual-Perspective Disentangled Multi-Intent Alignment for Enhanced Collaborative Filtering
DMICF models interactions from user- and item-centric perspectives with a macro-micro prototype-aware variational encoder and dimension-wise intent alignment to improve collaborative filtering.
- A More Accurate Algorithm Comparison through A/B Testing using Offline Evaluation Methods