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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
An integrated framework using ensemble hybrid ranking models and monotonicity-based extrapolation enables personalized policy deployment in a constrained two-sided job marketplace, achieving significant target metric improvements with guardrail compliance.
citing papers explorer
-
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.
-
Personalizing Marketplace Policies with Competing Objectives and Constrained Experiments: Evidence from a Job Marketplace
An integrated framework using ensemble hybrid ranking models and monotonicity-based extrapolation enables personalized policy deployment in a constrained two-sided job marketplace, achieving significant target metric improvements with guardrail compliance.