NSGR is a tree-structured generative reranker that progressively generates optimal lists via next-scale expansion and multi-scale neighbor loss to balance perspectives and align training signals.
In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.IR 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
SCASRec unifies ranking and redundancy elimination for route lists via stepwise corrective rewards and an adaptive end-of-recommendation token, claiming SOTA results on two datasets and real deployment.
A re-ranking system for rich-media search that plans query intents from sessions, adds visual signals from VLMs, and uses an LLM to score results on multiple facets before multi-task RL adaptation, with reported gains in engagement after industrial deployment.
citing papers explorer
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Next-Scale Generative Reranking: A Tree-based Generative Rerank Method at Meituan
NSGR is a tree-structured generative reranker that progressively generates optimal lists via next-scale expansion and multi-scale neighbor loss to balance perspectives and align training signals.
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SCASRec: A Self-Correcting and Auto-Stopping Model for Generative Route List Recommendation
SCASRec unifies ranking and redundancy elimination for route lists via stepwise corrective rewards and an adaptive end-of-recommendation token, claiming SOTA results on two datasets and real deployment.
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Rich-Media Re-Ranker: A User Satisfaction-Driven LLM Re-ranking Framework for Rich-Media Search
A re-ranking system for rich-media search that plans query intents from sessions, adds visual signals from VLMs, and uses an LLM to score results on multiple facets before multi-task RL adaptation, with reported gains in engagement after industrial deployment.