Interactive recommender agents achieve only 56% accuracy because they don't expand user knowledge during conversation—the bottleneck is preference formation, not item search.
Summary
If you're building agentic recommendation or preference-elicitation systems, this paper quantifies a hard constraint: clarifying questions alone don't work when users lack domain knowledge. Your agent needs explicit teaching mechanisms (examples, explanations) to move the needle on task specification.
Why it matters
If you're building agentic recommendation or preference-elicitation systems, this paper quantifies a hard constraint: clarifying questions alone don't work when users lack domain knowledge. Your agent needs explicit teaching mechanisms (examples, explanations) to move the needle on task specification.
Implementation verdict
This doesn't replace existing systems yet—it's a diagnostic. CoShop benchmark reveals that five-turn interactions with frontier models don't actually educate users about their own preferences. If you're shipping an agent that relies on user clarity, this is a warning: invest in knowledge-building dialog actions before optimizing search.
Sources
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