TY - GEN
T1 - Eliciting User Food Preferences in terms of Taste and Texture in Spoken Dialogue Systems
AU - Zeng, Jie
AU - Nakano, Yukiko I.
AU - Morita, Takeshi
AU - Kobayashi, Ichiro
AU - Yamaguchi, Takahira
N1 - Funding Information:
This work was supported by CREST, JST.
Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/10/16
Y1 - 2018/10/16
N2 - 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.
AB - 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.
KW - Spoken dialogue system
KW - Taste and texture
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85056655745&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056655745&partnerID=8YFLogxK
U2 - 10.1145/3279954.3279959
DO - 10.1145/3279954.3279959
M3 - Conference contribution
AN - SCOPUS:85056655745
T3 - MHFI 2018 - 3rd Workshop on Multisensory Approaches to Human-Food Interaction
BT - MHFI 2018 - 3rd Workshop on Multisensory Approaches to Human-Food Interaction
A2 - Velasco, Carlos
A2 - Nijholt, Anton
A2 - Obrist, Marianna
A2 - Okajima, Katsunori
A2 - Spence, Charles
PB - Association for Computing Machinery, Inc
T2 - 3rd Workshop on Multisensory Approaches to Human-Food Interaction, MHFI 2018, in conjunction with the 20th ACM International Conference on Multimodal Interaction, ICMI 2018
Y2 - 16 October 2018
ER -