Eliciting User Food Preferences in terms of Taste and Texture in Spoken Dialogue Systems
Jie Zeng, Yukiko Nakano, Takeshi Morita, Ichiro Kobayashi, Takahira Yamaguchi · 2018
Food preference varies from person to person and is not easy to verbalize. This study proposes a dialogue system that elicits the user's food preference through human-robot interaction. First, as the default knowledge of the dialogue system, we determined the ingredients of each dish from a large-scale recipe database, and collected the taste and texture of each dish and its ingredients by analyzing a large number of Twitter messages. Subsequently, the dialogue system asks questions to elicit the user's preferred taste/texture of the food by using the default knowledge base, while employing frame-based dialogue management. Finally, we created a food vector space that represents the relationship between the dish names, ingredients, and taste/texture expressions. We also discuss the possibility of using this vector space in dish recommendation.