GenRecEdit injects cold-start items into generative recommendation models via context-aware token editing and interference-reducing triggers, boosting cold-start accuracy while using only 9.5% of retraining time.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
PGHS fuses policy-guided LLM reasoning and ML fitting to simulate group user behavior with 8.8% error on Meituan data from 101 merchants and 26k trajectories, beating pure reasoning and fitting baselines by 45.8% and 40.9%.
Agent4POI generates context-conditioned multimodal affordance representations via a four-phase LLM agent, achieving 23.2% relative gains over baselines on POI benchmarks with reduced degradation under context shifts.
citing papers explorer
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GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items
GenRecEdit injects cold-start items into generative recommendation models via context-aware token editing and interference-reducing triggers, boosting cold-start accuracy while using only 9.5% of retraining time.
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Meituan Merchant Business Diagnosis via Policy-Guided Dual-Process User Simulation
PGHS fuses policy-guided LLM reasoning and ML fitting to simulate group user behavior with 8.8% error on Meituan data from 101 merchants and 26k trajectories, beating pure reasoning and fitting baselines by 45.8% and 40.9%.
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Agent4POI: Agentic Context-Conditioned Affordance Reasoning for Multimodal Point-of-Interest Recommendation
Agent4POI generates context-conditioned multimodal affordance representations via a four-phase LLM agent, achieving 23.2% relative gains over baselines on POI benchmarks with reduced degradation under context shifts.