UCSD study: synthetic dialogues from non-conversational data bootstrap recommender systems
UCSanDiego · hf · 2026-09-04
A UC San Diego empirical study on zero-data bootstrapping for conversational recommender systems (CRS) shows that domain signals from non-conversational sources can generate synthetic dialogue data that outperforms both zero-shot and scarce real-data baselines — offering a practical path for cold-start scenarios lacking dialogue data.
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