D2D adaptively prioritizes informative attribute queries and times recommendations in conversational search, yielding 22-30% higher target accuracy and shorter conversations than baselines in simulations.
Learning to Ask: Conversational Product Search via Representation Learning , volume=
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Dialogue to Discovery: Attribute-Aware Preference Elicitation for Conversational Product Search Assistants
D2D adaptively prioritizes informative attribute queries and times recommendations in conversational search, yielding 22-30% higher target accuracy and shorter conversations than baselines in simulations.