Expressing Robot’s Understanding of Human Preference Based on Successive Estimations during Dialog

Abstract

Conversational recommendation systems are crucial for making recommendations agreeable to the user. To reach an agreeable recommendation, this study proposes a dialog strategy that represents a reasonable order of items and elicits the current estimation by the wording of utterances based on the subjective preference estimation. We developed two dialog functions for the topic and word choice based on the history of preference estimations. The human impression of a robot’s diligence, understanding capability, and satisfaction were evaluated through a conversation with a virtual robot using a crowdsourcing platform. We compared six conditions that differed based on two topics and three wording patterns. The experimental results indicated that the main effect of the wording patterns, whereas one of the topic choices was not found to be significant. Further analysis showed that accurate estimation improves the robot’s impression when demonstrating its diligence.