SA²CRQ uses sequential adaptive residual quantization based on path entropy plus anchored curriculum regularization from head items to improve both efficiency and cold-start performance in generative retrieval.
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cs.IR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Synthetic data generated via LLM query rewriting improves retrieval recall and user experience for long-tail knowledge-intensive queries in e-commerce search.
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Towards Efficient and Generalizable Retrieval: Adaptive Semantic Quantization and Residual Knowledge Transfer
SA²CRQ uses sequential adaptive residual quantization based on path entropy plus anchored curriculum regularization from head items to improve both efficiency and cold-start performance in generative retrieval.
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Synthetic Data Powers Product Retrieval for Long-tail Knowledge-Intensive Queries in E-commerce Search
Synthetic data generated via LLM query rewriting improves retrieval recall and user experience for long-tail knowledge-intensive queries in e-commerce search.