A data-centric system for banking search recommendation combines synthetic query generation, intent disambiguation, and gap analysis, with synthetic data matching real data on Clinc150 but degrading on Banking77 and proprietary data.
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Enhancing and Scaling Search Query Datasets for Recommendation Systems
A data-centric system for banking search recommendation combines synthetic query generation, intent disambiguation, and gap analysis, with synthetic data matching real data on Clinc150 but degrading on Banking77 and proprietary data.