@inproceedings{aa0e3180ff9f43df8b5b85514b531d1c,
title = "Eliciting User Food Preferences in terms of Taste and Texture in Spoken Dialogue Systems",
abstract = "Food preference varies from person to person and is not easy to verbalize. This study proposes a dialogue system that elicits the user{\textquoteright}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{\textquoteright}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.",
keywords = "Spoken dialogue system, Taste and texture, Twitter",
author = "Jie Zeng and Nakano, {Yukiko I.} and Takeshi Morita and Ichiro Kobayashi and Takahira Yamaguchi",
year = "2018",
month = oct,
day = "16",
doi = "10.1145/3279954.3279959",
language = "English",
series = "MHFI 2018 - 3rd Workshop on Multisensory Approaches to Human-Food Interaction",
publisher = "Association for Computing Machinery, Inc",
editor = "Carlos Velasco and Anton Nijholt and Marianna Obrist and Katsunori Okajima and Charles Spence",
booktitle = "MHFI 2018 - 3rd Workshop on Multisensory Approaches to Human-Food Interaction",
note = "3rd Workshop on Multisensory Approaches to Human-Food Interaction, MHFI 2018, in conjunction with the 20th ACM International Conference on Multimodal Interaction, ICMI 2018 ; Conference date: 16-10-2018",
}