Cross-domain Semantic IDs from organic feed activity, quantized via RQ-FSQ, improve industrial ads CTR prediction with gains up to +0.351% AUC at 30x smaller storage.
Enhancing embedding representation stability in recommendation systems with semantic ID.arXiv preprint arXiv:2504.02137, 2025
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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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Quantizing Intent: Cross-Domain Semantic IDs from Organic Activity for Industrial Ranking
Cross-domain Semantic IDs from organic feed activity, quantized via RQ-FSQ, improve industrial ads CTR prediction with gains up to +0.351% AUC at 30x smaller storage.
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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.