research-article

Eliciting User Food Preferences in terms of Taste and Texture in Spoken Dialogue Systems

Authors:

Yukiko I. Nakano,

Takeshi Morita,

Ichiro Kobayashi,

Takahira YamaguchiAuthors Info & Claims

MHFI'18: Proceedings of the 3rd International Workshop on Multisensory Approaches to Human-Food Interaction

Article No.: 6, Pages 1 - 5

https://doi.org/10.1145/3279954.3279959

Published: 16 October 2018 Publication History

Abstract

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.

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Cited By

Reimann MKunneman FOertel CHindriks K(2024)A Survey on Dialogue Management in Human-robot InteractionACM Transactions on Human-Robot Interaction10.1145/364860513:2(1-22)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3648605
Sakai KNakamura YYoshikawa YKano SIshiguro H(2023)Expressing Robot’s Understanding of Human Preference Based on Successive Estimations during DialogInternational Journal of Human–Computer Interaction10.1080/10447318.2023.223219540:18(5139-5160)Online publication date: 10-Jul-2023
https://doi.org/10.1080/10447318.2023.2232195
Jannach D(2022)Evaluating conversational recommender systemsArtificial Intelligence Review10.1007/s10462-022-10229-x56:3(2365-2400)Online publication date: 12-Jul-2022
https://doi.org/10.1007/s10462-022-10229-x
Show More Cited By

Index Terms

Eliciting User Food Preferences in terms of Taste and Texture in Spoken Dialogue Systems
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Natural language interfaces

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cover image ACM Other conferences

MHFI'18: Proceedings of the 3rd International Workshop on Multisensory Approaches to Human-Food Interaction

October 2018

59 pages

ISBN:9781450360746

DOI:10.1145/3279954

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: Specialist Interest Group in Computer-Human Interaction of the ACM

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 October 2018

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ICMI '18

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SIGCHI

ICMI '18: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

October 16, 2018

CO, Boulder, USA

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Overall Acceptance Rate 6 of 8 submissions, 75%

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Cited By

Reimann MKunneman FOertel CHindriks K(2024)A Survey on Dialogue Management in Human-robot InteractionACM Transactions on Human-Robot Interaction10.1145/364860513:2(1-22)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3648605
Sakai KNakamura YYoshikawa YKano SIshiguro H(2023)Expressing Robot’s Understanding of Human Preference Based on Successive Estimations during DialogInternational Journal of Human–Computer Interaction10.1080/10447318.2023.223219540:18(5139-5160)Online publication date: 10-Jul-2023
https://doi.org/10.1080/10447318.2023.2232195
Jannach D(2022)Evaluating conversational recommender systemsArtificial Intelligence Review10.1007/s10462-022-10229-x56:3(2365-2400)Online publication date: 12-Jul-2022
https://doi.org/10.1007/s10462-022-10229-x
Jannach DManzoor ACai WChen L(2021)A Survey on Conversational Recommender SystemsACM Computing Surveys10.1145/345315454:5(1-36)Online publication date: 20-May-2021
https://doi.org/10.1145/3453154
Seiderer ARitschel HAndré EPrandi CMarquez-Barja J(2020)Development of a Privacy-By-Design Speech Assistant Providing Nutrient Information for German SeniorsProceedings of the 6th EAI International Conference on Smart Objects and Technologies for Social Good10.1145/3411170.3411227(114-119)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.1145/3411170.3411227
Min WJiang SJain R(2020)Food Recommendation: Framework, Existing Solutions, and ChallengesIEEE Transactions on Multimedia10.1109/TMM.2019.295876122:10(2659-2671)Online publication date: Oct-2020
https://doi.org/10.1109/TMM.2019.2958761
Nijholt AVelasco CObrist MOkajima KSpence CD'Mello SGeorgiou PScherer SProvost ESoleymani MWorsley M(2018)3rd International Workshop on Multisensory Approaches to Human-Food InteractionProceedings of the 20th ACM International Conference on Multimodal Interaction10.1145/3242969.3265860(657-659)Online publication date: 2-Oct-2018
https://dl.acm.org/doi/10.1145/3242969.3265860

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