Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems
Shuo Zhang,
Mu-Chun Wang,
Krisztian Balog
Abstract:User simulation has been a cost-effective technique for evaluating conversational recommender systems. However, building a humanlike simulator is still an open challenge. In this work, we focus on how users reformulate their utterances when a conversational agent fails to understand them. First, we perform a user study, involving five conversational agents across different domains, to identify common reformulation types and their transition relationships. A common pattern that emerges is that persistent users … Show more
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