Abstract
We describe a user study evaluating two critiquing-based recommender
agents based on three criteria: decision accuracy, decision effort,
and user confidence. Results show that user-motivated critiques were
more frequently applied and the example critiquing system employing
only this type of critiques achieved the best results. In particular,
the example critiquing agent significantly improves users' decision
accuracy with less cognitive effort consumed than the dynamic critiquing
recommender with system-proposed critiques. Additionally, the former
is more likely to inspire users' confidence of their choice and promote
their intention to purchase and return to the agent for future use.
Copyright © 2006, American Association for Artificial Intelligence
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