demonstration

Market2Dish: A Health-aware Food Recommendation System

Authors:

Changsheng XuAuthors Info & Claims

MM '19: Proceedings of the 27th ACM International Conference on Multimedia

Pages 2188 - 2190

https://doi.org/10.1145/3343031.3350594

Published: 15 October 2019 Publication History

Abstract

In order to help people develop healthy eating habits, we present a personalized health-aware food recommendation system, calledMarket2Dish. Market2Dish could recognize the ingredients in the micro-videos taken from the market, characterize the health conditions of users from their social media accounts, and ultimately recommend users with the personalized healthy foods. Specifically, we employ a word-class interaction based text classification model to learn the fine-grained similarity between sparse health features on the social media platforms and pre-defined health concepts, and then a category-aware hierarchical memory network based recommender is introduced to learn the user-recipe interactions for better food recommendations. Moreover, we demonstrate this system as an online app for real-time interactions with users.

References

[1]

Yongsheng An, Yu Cao, Jingjing Chen, Chong-Wah Ngo, Jia Jia, Huanbo Luan, and Tat-Seng Chua. 2017. PIC2DISH: A customized cooking assistant system. In Proceedings of the 25th International Conference on Multimedia. ACM, 1269--1273.

Digital Library

[2]

Jing-Jing Chen, Chong-Wah Ngo, Fu-Li Feng, and Tat-Seng Chua. 2018. Deep understanding of cooking procedure for cross-modal recipe retrieval. In Proceedings of the 26th International Conference on Multimedia. ACM, 1020--1028.

Digital Library

[3]

Chen Cui, Wenjie Wang, Xuemeng Song, Minlie Huang, Xin-Shun Xu, and Liqiang Nie. 2019. User Attention-guided Multimodal Dialog Systems. In Proceedings of the 42Nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 445--454.

Digital Library

[4]

David Elsweiler, Christoph Trattner, and Morgan Harvey. 2017. Exploiting food choice biases for healthier recipe recommendation. In Proceedings of the 40th International SIGIR Conference on Research and Development in Information Retrieval. ACM, 575--584.

Digital Library

[5]

Aleksandr Farseev and Tat-Seng Chua. 2017. Tweet can be fit: Integrating data from wearable sensors and multiple social networks for wellness profile learning. ACM Transactions on Information Systems, Vol. 35, 4 (2017), 42.

Digital Library

[6]

Mouzhi Ge, Francesco Ricci, and David Massimo. 2015. Health-aware food recommender system. In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 333--334.

Digital Library

[7]

Hokuto Kagaya, Kiyoharu Aizawa, and Makoto Ogawa. 2014. Food detection and recognition using convolutional neural network. In Proceedings of the 22nd International Conference on Multimedia. ACM, 1085--1088.

Digital Library

[8]

Yoshiyuki Kawano and Keiji Yanai. 2014. Foodcam-256: a large-scale real-time mobile food recognition system employing high-dimensional features and compression of classifier weights. In Proceedings of the 22nd International Conference on Multimedia. ACM, 761--762.

Digital Library

[9]

Keigo Kitamura, Toshihiko Yamasaki, and Kiyoharu Aizawa. 2008. Food log by analyzing food images. In Proceedings of the 16th International Conference on Multimedia. ACM, 999--1000.

Digital Library

[10]

Tomasz Kusmierczyk, Christoph Trattner, and Kjetil Nørvåg. 2015. Temporality in online food recipe consumption and production. In Proceedings of the 24th International Conference on World Wide Web. ACM, 55--56.

Digital Library

[11]

Weiqing Min, Shuqiang Jiang, Shuhui Wang, Jitao Sang, and Shuhuan Mei. 2017. A delicious recipe analysis framework for exploring multi-modal recipes with various attributes. In Proceedings of the 25th International Conference on Multimedia. ACM, 402--410.

Digital Library

[12]

Ferda Ofli, Yusuf Aytar, Ingmar Weber, Raggi Al Hammouri, and Antonio Torralba. 2017. The food perception gap on instagram and its relation to health. In Proceedings of the 26th International Conference on World Wide Web. ACM, 509--518.

Digital Library

[13]

Satoshi Sanjo and Marie Katsurai. 2017. Recipe popularity prediction with deep visual-semantic fusion. In Proceedings of the Conference on Information and Knowledge Management. ACM, 2279--2282.

Digital Library

[14]

Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE, 2818--2826.

[15]

Christoph Trattner and David Elsweiler. 2017. Investigating the healthiness of internet-sourced recipes: implications for meal planning and recommender systems. In Proceedings of the 26th International Conference on World Wide Web. ACM, 489--498.

Digital Library

[16]

Liping Wang, Qing Li, Na Li, Guozhu Dong, and Yu Yang. 2008. Substructure similarity measurement in Chinese recipes. In Proceedings of the 17th International Conference on World Wide Web. ACM, 979--988.

Digital Library

[17]

Shuohang Wang and Jing Jiang. 2016. Learning natural language inference with LSTM. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics . ACL, 1442--1451.

[18]

Wenjie Wang, Minlie Huang, Xin-Shun Xu, Fumin Shen, and Liqiang Nie. 2018. Chat more: Deepening and widening the chatting topic via a deep model. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 255--264.

Digital Library

[19]

Hui Wu, Michele Merler, Rosario Uceda-Sosa, and John R Smith. 2016. Learning to make better mistakes: Semantics-aware visual food recognition. In Proceedings of the 24th International Conference on Multimedia. ACM, 172--176.

Digital Library

[20]

Shulin Yang, Mei Chen, Dean Pomerleau, and Rahul Sukthankar. 2010. Food recognition using statistics of pairwise local features. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2249--2256.

Cited By

Islam TJoyita AAlam MMehedi Hassan MHassan MGravina R(2023)Human-Behavior-Based Personalized Meal Recommendation and Menu Planning Social SystemIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.321350610:4(2099-2110)Online publication date: Aug-2023
https://doi.org/10.1109/TCSS.2022.3213506
Thammabunwarit NMaliyaem MVatcharaphrueksadee ASakul-Ung PViriyapant K(2023)Active Filtering Co-occurrence and Ontology Graph: AFCO for Search Algorithm2023 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C)10.1109/RI2C60382.2023.10356035(1-5)Online publication date: 24-Aug-2023
https://doi.org/10.1109/RI2C60382.2023.10356035
Nath NSalunke RMishra W(2023)Smart Refrigerator System: Enabling Healthy CookingDesign in the Era of Industry 4.0, Volume 110.1007/978-981-99-0293-4_87(1081-1091)Online publication date: 25-Jul-2023
https://doi.org/10.1007/978-981-99-0293-4_87
Show More Cited By

Index Terms

Market2Dish: A Health-aware Food Recommendation System
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Market2Dish: Health-aware Food Recommendation
With the rising incidence of some diseases, such as obesity and diabetes, the healthy diet is arousing increasing attention. However, most existing food-related research efforts focus on recipe retrieval, user-preference-based food recommendation, cooking ...
Multi-modal Food Recommendation with Health-aware Knowledge Distillation
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management

Food recommendation systems play a pivotal role in shaping dietary salubrity and fostering sustainable lifestyles by recommending recipes and foodstuffs that align with user preferences. Metadata information of a recipe, encompassing multi-modal ...
"Easy" Cooking Recipe Recommendation Considering User's Conditions
WI-IAT '09: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03

It is natural to think that couples who work at a company or a person who lives by her/himself want to cook food for themselves as quickly and easily as possible when they are busy. However, to keep having the same food they can easily cook fed them up, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '19: Proceedings of the 27th ACM International Conference on Multimedia

October 2019

2794 pages

ISBN:9781450368896

DOI:10.1145/3343031

General Chairs:
Laurent Amsaleg
CNRS-IRISA, France
,
Benoit Huet
EURECOM, France
,
Martha Larson
Radboud University and TU Delft (Netherlands)
,
Program Chairs:
Guillaume Gravier
CNRS-IRISA, France
,
Hayley Hung
Delft University of Technology Netherlands
,
Chong-Wah Ngo
City University of Hong Kong Hong Kong
,
Wei Tsang Ooi
National University of Singapore Singapore

Copyright © 2019 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2019

Check for updates

Author Tags

Qualifiers

Demonstration

Funding Sources

the Tencent AI Lab Rhino-Bird Joint Research Program
the Project of Thousand Youth Talents 2016
the National Natural Science Foundation of China

Conference

MM '19

Sponsor:

SIGMM

MM '19: The 27th ACM International Conference on Multimedia

October 21 - 25, 2019

Nice, France

Acceptance Rates

MM '19 Paper Acceptance Rate 252 of 936 submissions, 27%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
682
Total Downloads

Downloads (Last 12 months)55
Downloads (Last 6 weeks)1

Reflects downloads up to 03 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Islam TJoyita AAlam MMehedi Hassan MHassan MGravina R(2023)Human-Behavior-Based Personalized Meal Recommendation and Menu Planning Social SystemIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.321350610:4(2099-2110)Online publication date: Aug-2023
https://doi.org/10.1109/TCSS.2022.3213506
Thammabunwarit NMaliyaem MVatcharaphrueksadee ASakul-Ung PViriyapant K(2023)Active Filtering Co-occurrence and Ontology Graph: AFCO for Search Algorithm2023 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C)10.1109/RI2C60382.2023.10356035(1-5)Online publication date: 24-Aug-2023
https://doi.org/10.1109/RI2C60382.2023.10356035
Nath NSalunke RMishra W(2023)Smart Refrigerator System: Enabling Healthy CookingDesign in the Era of Industry 4.0, Volume 110.1007/978-981-99-0293-4_87(1081-1091)Online publication date: 25-Jul-2023
https://doi.org/10.1007/978-981-99-0293-4_87
Tian YZhang CMetoyer RChawla N(2022)Recipe Recommendation With Hierarchical Graph Attention NetworkFrontiers in Big Data10.3389/fdata.2021.7784174Online publication date: 12-Jan-2022
https://doi.org/10.3389/fdata.2021.778417
Srijayanthi SDeepa CKiruthika NManibabu R(2022)DIET RECOMMENDATION FORPATIENTS USING K-MEANS2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC)10.1109/IIHC55949.2022.10060429(1380-1385)Online publication date: 18-Nov-2022
https://doi.org/10.1109/IIHC55949.2022.10060429
Islam TJoyita AAnanna AKhandoker RAhsan FAlam M(2022)Food Affection Determination Through Biosignal Based Affective Computing2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)10.1109/CSDE56538.2022.10089292(1-5)Online publication date: 18-Dec-2022
https://doi.org/10.1109/CSDE56538.2022.10089292
Chiu SChen YLiou YLee CYing JLoh CLin J(2021)A Fast, Interactive, Location-Based Food Recommendation ApplicationProceedings of the 8th Multidisciplinary International Social Networks Conference10.1145/3504006.3504010(21-25)Online publication date: 15-Nov-2021
https://dl.acm.org/doi/10.1145/3504006.3504010
Pacifico LBritto LLudermir TPereira Ada Rocha L(2021)Ingredient Substitute Recommendation Based on Collaborative Filtering and Recipe Context for Automatic Allergy-Safe Recipe GenerationProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3470482.3479622(97-104)Online publication date: 5-Nov-2021
https://dl.acm.org/doi/10.1145/3470482.3479622
Wang WDuan LJiang HJing PSong XNie L(2021)Market2Dish: Health-aware Food RecommendationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/341821117:1(1-19)Online publication date: 16-Apr-2021
https://dl.acm.org/doi/10.1145/3418211
Prajapati JBrahmbhatt K(2021)A Systematic Literature Review on Health Recommender SystemsProceedings of the International e-Conference on Intelligent Systems and Signal Processing10.1007/978-981-16-2123-9_43(557-572)Online publication date: 14-Aug-2021
https://doi.org/10.1007/978-981-16-2123-9_43
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten