Adaptive Re-Ranking trains a classifier to route queries to BM25, MiniLM-L6-v2, or BGE-v2-m3 based on a utility label, yielding 1.15-53x lower median latency and competitive nDCG@10 versus always using the heaviest model.
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User-specific behavioral signals, especially prior search queries, outperform population-level demand patterns and static profiles for inferring gender, age, category, and size from underspecified e-commerce queries.
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Adaptive Re-Ranking
Adaptive Re-Ranking trains a classifier to route queries to BM25, MiniLM-L6-v2, or BGE-v2-m3 based on a utility label, yielding 1.15-53x lower median latency and competitive nDCG@10 versus always using the heaviest model.
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IntentTune: Using user demand and personalization to resolve "unknown" query intents for e-commerce search
User-specific behavioral signals, especially prior search queries, outperform population-level demand patterns and static profiles for inferring gender, age, category, and size from underspecified e-commerce queries.