More Web Proxy on the site http://driver.im/

research-article

Deriving a recipe similarity measure for recommending healthful meals

Authors:

Youri van Pinxteren,

Gijs Geleijnse,

Paul KamsteegAuthors Info & Claims

IUI '11: Proceedings of the 16th international conference on Intelligent user interfaces

Pages 105 - 114

https://doi.org/10.1145/1943403.1943422

Published: 13 February 2011 Publication History

Abstract

A recipe recommender system may stimulate healthful and varied eating, when the presented recipes fit the lifestyle of the user. As consumers face the barrier to change their eating and cooking behavior, we aim for a strategy to provide more healthful variations to routine recipes. In this paper, a similarity measure for recipes is derived by taking a user-centered approach. Such a measure can be used to recommend healthier alternatives to commonly selected meals, which are perceived to be similar. Recipes presented using this strategy may fit the demand for health and variation within the boundaries of a busy lifestyle. Having derived and evaluated a recipe similarity measure, we explore its use through an at-home trial.

References

[1]

A. Beltran, K. Knight Sepulveda, K. Watson, T. Baranowski, J. Baranowski, N. Islam, and M. Missaghian. Diverse food items are similarly categorized by 8- to 13-year-old children. Journal of Nutrition Education and Behavior, 40(3):149 -- 159, 2008.

[2]

S. Borra, L. Kelly, M. Tuttle, and K. Neville. Developing actionable dietary guidance messages: Dietary fat as a case study. J. of the Am. Diet. Ass., 101:679 - 684, 2001.

[3]

L. Bouwman, H. te Molder, M. M. Koelen, and C. M. van Woerkuma. I eat healthfully but I am not a freak. Appetite, in press, available online, 2009.

[4]

J. Brug, A. Oenema, and M. Campbell. Past, present, and future of computer-tailored nutrition education. Am J Clin Nutr 2003;77(suppl):1028S--34S, 2003.

[5]

J. Burg, I. Steenhuis, P. van Assema, H. de Vries. The Impact of a Computer-Tailored Nutrition Intervention. Preventive Medicine 25:236 -- 242, 1996.

[6]

Canadian Medical Association. 9th Annual National Report Card on Health Care. 2009.

[7]

J. S. Downs, G. Loewenstein, and J. Wisdom. Strategies for promoting healthier food choices. American Economic Review: Papers and Proceedings. The Psychology of Food Consumption, 99:1 - 6, 2009.

[8]

A. Drewnowski, S.A. Henderson,A. Driscoll and B.J. Rolls. The Dietary Variety Score: assessing diet quality in healthy young and older adults. J. of Am. Diet Ass., 97(3):266--71, 1997.

[9]

P. Faye, D. Bremaud, M.D. Daubin, P. Courcoux, A. Giboreau, and H. Nicod. Perceptive free sorting and verbalization tasks with naive subjects: an alternative to descriptive mappings. Food Quality and Preference, 15(7-8):781 -- 791, 2004.

[10]

J. Freyne and S. Berkovsky. Intelligent food planning: personalized recipe recommendation. In IUI '10: Proceeding of the 14th international conference on Intelligent user interfaces, pages 321--324, New York, NY, USA, 2010. ACM.

Digital Library

[11]

G. Geleijnse, Th. Overbeek, N. van der Veeken and M. Willemsen. Extracting Vegetable Information from Recipes to Facilitate Health-Aware Choices. In proceedings of the Fifth International Conference on Persuasive Technology, Copenhagen, Denmark, 2010.

[12]

G. Geleijnse, L. Wang, P. Nachtigall, J. Hoonhout and Q. Li. Promoting Tasty Meals to Support Healthful Eating. In proceedings of Wellness Informatics workshop at CHI2010, Atlanta, GA, 2010.

[13]

K.J. Hammond. CHEF: A model of case-based planning. Proc. AAAI-86, 1986.

[14]

L. Lähteenmäki and H.C.M. Van Trijp. Hedonic responses, variety-seeking tendency and expressed variety in sandwich choices. Appetite, 24(2):139 -- 151, 1995.

[15]

H.T. Lawless, N. Sheng, and S. Knoops. Multidimensional scaling of sorting data applied to cheese perception. Food Quality and Preference, 6(2):91 -- 98, 1995.

[16]

M. Lelievre, S. Chollet, H. Abdi, and D. Valentin. What is the validity of the sorting task for describing beers? a study using trained and untrained assessors. Food Quality and Preference, 19(8):697 -- 703, 2008. Seventh Rose Marie Pangborn Sensory Science Symposium.

[17]

P. Nachtigall, G. Geleijnse, J. Hoonhout, A. van Halteren and N. Gros. Toward an intelligent nutritional support system. In proceedings of Wellness Informatics workshop at CHI2010, Atlanta, GA, 2010.

[18]

M. Svensson, K. Höök, and R. Cöster. Designing and evaluating kalas: A social navigation system for food recipes. ACM Trans. Comput.-Hum. Interact., 12(3):374--400, 2005.

Digital Library

[19]

R.H. Thaler and C.R. Sustein. Nudge: Improving Decision about Health, Wealth and Happiness. Yale University Press, 2008.

[20]

S. Thiele and C. Weiss. Consumer demand for food diversity: evidence for Germany. Food Policy 28: 99--115, 2003.

[21]

M. Twigt. Healthy eating & eating varied. Master's thesis, Wageningen University, the Netherlands, 2009.

[22]

W. van Hage, M. Sini, L. Finch, H.P. Kolb and A. Schreiber. The OAEI Food Task: an analysis of a food alignment task. Applied Ontology 5(1), 2010.

Digital Library

[23]

M. Wabeke. Creativity in the kitchen. Master's thesis, Wageningen University, the Netherlands, December 2008.

[24]

L. Wang, Q. Li, N. Li, G. Dong, and Y. Yang. Substructure similarity measurement in Chinese recipes. In WWW'08: Proceeding of the 17th international conference on WorldWideWeb, pages 979--988, New York, NY, USA, 2008. ACM.

Digital Library

[25]

B. Wansink. Profiling Nutritional Gatekeepers. Food Quality and Preference. 14(4), 289--297, 2003.

[26]

B. Wansink and P. Chandon. Can "low fat" nutrition labels lead to obesity? Journal of Marketing Research, X(III):605 -- 617, 2006.

[27]

D. M. Winham. Culturally Tailored Foods and Cardiovascular Disease Prevention. American journal of lifestyle medicine, 3:64, 2009.

[28]

J. Wisdom, J.S. Downs and G. Loewenstein. Promoting Healthy Choices: Information vs. Convenience. American Economic Journal: Applied Economics, 2(2): 164--178, 2010.

[29]

World Health Organization. Food and health in Europe: a new basis for action.

[30]

Q. Zhang, R. Hu, B. MacNamee, and S. J. Delany. Back to the future: Knowledge light case base cookery. In ECCBR 2008, The 9th European Conference on Case-Based Reasoning, Workshop Proceedings, pages 239--248, Trier, Germany, 2008.

Cited By

Li MLi LTao XHuang JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)MealRec+: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and HealthinessProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657857(564-574)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657857
Das PBhattacharya R(2024)A Random Walk-Based Approach for Clustering of Food ItemsCyber Intelligence and Information Retrieval10.1007/978-981-97-3594-5_32(385-395)Online publication date: 19-Jul-2024
https://doi.org/10.1007/978-981-97-3594-5_32
Howell PAryal AWu C(2023)Web-Based Patient Recommender Systems for Preventive Care: Protocol for Empirical Research PropositionsJMIR Research Protocols10.2196/4331612(e43316)Online publication date: 30-Mar-2023
https://doi.org/10.2196/43316
Show More Cited By

Index Terms

Deriving a recipe similarity measure for recommending healthful meals
1. Information systems

Recommendations

Recipe Recommendation Method by Similarity Measure with Food Image Recognition
ICISDM '22: Proceedings of the 6th International Conference on Information System and Data Mining

This paper presents a recipe recommendation method by similarity measure with food image recognition. In general, it is difficult for users with little cooking experience to find out what kind of dishes they can make from the ingredients they currently ...
Information Extraction from Unstructured Recipe Data
ICCTA '19: Proceedings of the 2019 5th International Conference on Computer and Technology Applications

Online food recipes are an important source of information for many individuals, who use these to learn how to cook new dishes and choose their meals. However, these often lack structured information, useful to improve search and recommendation systems ...
A recipe recommendation system that considers user's mood
iiWAS '16: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services

Homemaker decide what to cook based on the mood they are in, the ingredients they have in their refrigerators, or the ingredients offered in a supermarket. Most of the existing services for searching recipes allow ingredient names or recipe names as ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

IUI '11: Proceedings of the 16th international conference on Intelligent user interfaces

February 2011

504 pages

ISBN:9781450304191

DOI:10.1145/1943403

General Chairs:
Pearl Pu
EPFL, Switzerland
,
Michael Pazzani
Rutgers University, USA
,
Program Chairs:
Elisabeth André
Augsburg University, Germany
,
Doug Riecken
CCLS Columbia University, USA

Copyright © 2011 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 February 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

IUI '11

Sponsor:

IUI '11: 16th International Conference on Intelligent User Interfaces

February 13 - 16, 2011

CA, Palo Alto, USA

Acceptance Rates

Overall Acceptance Rate 746 of 2,811 submissions, 27%

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

61
Total Citations
View Citations
1,109
Total Downloads

Downloads (Last 12 months)43
Downloads (Last 6 weeks)6

Reflects downloads up to 21 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Li MLi LTao XHuang JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)MealRec+: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and HealthinessProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657857(564-574)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657857
Das PBhattacharya R(2024)A Random Walk-Based Approach for Clustering of Food ItemsCyber Intelligence and Information Retrieval10.1007/978-981-97-3594-5_32(385-395)Online publication date: 19-Jul-2024
https://doi.org/10.1007/978-981-97-3594-5_32
Howell PAryal AWu C(2023)Web-Based Patient Recommender Systems for Preventive Care: Protocol for Empirical Research PropositionsJMIR Research Protocols10.2196/4331612(e43316)Online publication date: 30-Mar-2023
https://doi.org/10.2196/43316
Saikia PMangal AJain HSingh A(2023)Cuisine Prediction from Ingredients using Hyper Parameter Tuning on Machine Learning Algorithms2023 IEEE Silchar Subsection Conference (SILCON)10.1109/SILCON59133.2023.10404585(1-6)Online publication date: 3-Nov-2023
https://doi.org/10.1109/SILCON59133.2023.10404585
DOAN DTRAN TTRUONG BNGO TNGUYEN M(2023)A Personalized Meal Recommendation Approach For Smart Refrigerator2023 International Conference on Advanced Computing and Analytics (ACOMPA)10.1109/ACOMPA61072.2023.00027(113-118)Online publication date: 22-Nov-2023
https://doi.org/10.1109/ACOMPA61072.2023.00027
Ratisoontorn N(2022)Recipe Recommendations for Toddlers Using Integrated Nutritional and Ingredient Similarity Measures2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)10.1109/JCSSE54890.2022.9836248(1-6)Online publication date: 22-Jun-2022
https://doi.org/10.1109/JCSSE54890.2022.9836248
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
Martín MMacías J(2022)A Supporting Tool for Enhancing User’s Mental Model Elicitation and Decision-Making in User Experience ResearchInternational Journal of Human–Computer Interaction10.1080/10447318.2022.204188539:1(183-202)Online publication date: 18-Apr-2022
https://doi.org/10.1080/10447318.2022.2041885
Jesse MBauer CJannach D(2022)Intra-list similarity and human diversity perceptions of recommendations: the details matterUser Modeling and User-Adapted Interaction10.1007/s11257-022-09351-w33:4(769-802)Online publication date: 12-Dec-2022
https://doi.org/10.1007/s11257-022-09351-w
Haseena SSaroja SRevathi T(2022)A fuzzy approach for multi criteria decision making in diet plan ranking system using cuckoo optimizationNeural Computing and Applications10.1007/s00521-022-07163-y34:16(13625-13638)Online publication date: 31-Mar-2022
https://doi.org/10.1007/s00521-022-07163-y
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.

Media

Figures

Other

Tables

View Table of Contents