research-article

Automatic recipe cuisine classification by ingredients

Authors:

Han Su,

Ting-Wei Lin,

Cheng-Te Li,

Man-Kwan Shan,

Janet ChangAuthors Info & Claims

UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

Pages 565 - 570

https://doi.org/10.1145/2638728.2641335

Published: 13 September 2014 Publication History

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Abstract

With the growth of recipe sharing services, online cooking recipes associated with ingredients and cooking procedures are available. Many recipe sharing sites have devoted to the development of recipe recommendation mechanism. While most food related research has been on recipe recommendation, little effort has been done on analyzing the correlation between recipe cuisines and ingredients. In this paper, we aim to investigate the underlying cuisine-ingredient connections by exploiting the classification techniques, including associative classification and support vector machine. Our study conducted on food.com data provides insights about which cuisines are the most similar and what are the essential ingredients for a cuisine, with an application to automatic cuisine labeling for recipes.

References

[1]

M. Svensson, K. HooK, and R. Coster, Designing and Evaluating Kalas: A Social Navigation system for food recipes. ACM Transactions on Computer-Human Interaction Vol. 12, No. 3, 2005.

Digital Library

Google Scholar

[2]

M. Uede, M. Takahata, and S. Nakajima, User's Food Preference Extraction for Personalized Cooking Recipe Recommendation, 2nd International Workshop on Semantic Personalized Information Management: Retrieval and Recommendation SPIM, 2011.

Google Scholar

[3]

M. Ueda, M. Takahata, and S. Nakajima, Recipe Recommendation Method Based on User's Food Preferences, IADIS International Conference on e-Society, 2011.

Google Scholar

[4]

H. Xie, L. Yu, and Q. Li, A hybrid Semantic Item Model for Recipe Search by Example, IEEE International Symposium on Multimedia, 2010.

Digital Library

Google Scholar

[5]

P. Forbes and M. Zhu, Content-boosted Matrix Factorization for Recommender Systems: Experiments with Recipe Recommendation, ACM International Conference on Recommender Systems RecSys, 2011.

Digital Library

Google Scholar

[6]

L. Wang, Q. Li, N. Li, G. Dong, and Y. Yang, Substructure Similarity Measurement in Chinese Recipes, International World Wide Web Conference WWW, 2008.

Digital Library

Google Scholar

[7]

A. Veloso, W. Meira, and M. J. Zaki. Lazy Associative Classification, IEEE International Conference on Data Mining ICDM, 2006.

Digital Library

Google Scholar

[8]

C. C. Chang and C. J. Lin. LIBSVM: a library for support vector machines, ACM Transactions on Intelligent Systems and Technology TIST, 2011.

Digital Library

Google Scholar

[9]

R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules in Large Databases. International Conference on Very Large Data Bases VLDB, 1994.

Digital Library

Google Scholar

Cited By

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Anlauf SDorl SHirz TLasslberger MGrassmann RHimmelbauer JWinkler S(2024)Identification of similarities and clusters of bread baking recipes based on data of ingredientsInternational Journal of Food Engineering10.1515/ijfe-2023-0032Online publication date: 11-Mar-2024
https://doi.org/10.1515/ijfe-2023-0032
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
Alshanketi F(2023)Machine Learning Model for Predicting the Cuisine Category from a Dish Ingredients2023 International Conference on Smart Computing and Application (ICSCA)10.1109/ICSCA57840.2023.10087436(1-6)Online publication date: 5-Feb-2023
https://doi.org/10.1109/ICSCA57840.2023.10087436
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Index Terms

Automatic recipe cuisine classification by ingredients
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

September 2014

1409 pages

ISBN:9781450330473

DOI:10.1145/2638728

General Chairs:
AJ Brush
Microsoft Research
,
Adrian Friday
Lancaster University, UK
,
Program Chairs:
Julie Kientz
University of Washington
,
James Scott
Microsoft Research, UK
,
Junehwa Song
Korea Advanced Institute of Science and Technology (KAIST), KR

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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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2014

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September 13 - 17, 2014

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

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Anlauf SDorl SHirz TLasslberger MGrassmann RHimmelbauer JWinkler S(2024)Identification of similarities and clusters of bread baking recipes based on data of ingredientsInternational Journal of Food Engineering10.1515/ijfe-2023-0032Online publication date: 11-Mar-2024
https://doi.org/10.1515/ijfe-2023-0032
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
Alshanketi F(2023)Machine Learning Model for Predicting the Cuisine Category from a Dish Ingredients2023 International Conference on Smart Computing and Application (ICSCA)10.1109/ICSCA57840.2023.10087436(1-6)Online publication date: 5-Feb-2023
https://doi.org/10.1109/ICSCA57840.2023.10087436
Guria SBanerjee SBhattacharjee SPaul SHalder SDas P(2023)Classification of Foods based on Ingredients2023 International Conference on Computer Communication and Informatics (ICCCI)10.1109/ICCCI56745.2023.10128374(1-6)Online publication date: 23-Jan-2023
https://doi.org/10.1109/ICCCI56745.2023.10128374
Nijhawan RSinha GBatra AKumar MSharma H(2023)VTnet+Handcrafted based approach for food cuisines classificationMultimedia Tools and Applications10.1007/s11042-023-15800-483:4(10695-10715)Online publication date: 24-Jun-2023
https://doi.org/10.1007/s11042-023-15800-4
Roither AKurz MSonnleitner E(2022)The Chef’s Choice: System for Allergen and Style Classification in RecipesApplied Sciences10.3390/app1205259012:5(2590)Online publication date: 2-Mar-2022
https://doi.org/10.3390/app12052590
Butt SBakhtyar MNoor WBaber JUllah IAhmed ABasit AKakar MBalas V(2022)Semantic similarity based food entities recognition using WordNetJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21930643:2(2069-2078)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-219306
Tamaru STaki HUsuki RNakanishi T(2022)Recipe Recommendation Method by Similarity Measure with Food Image RecognitionProceedings of the 6th International Conference on Information System and Data Mining10.1145/3546157.3546170(81-88)Online publication date: 27-May-2022
https://dl.acm.org/doi/10.1145/3546157.3546170
Lăpuşan AHorge RPetres SDînşoreanu MPotolea RLemnaru C(2022)Instructions Are All You Need: Cooking Parameters Classification for Monolingual Recipes2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP)10.1109/ICCP56966.2022.10053948(73-80)Online publication date: 22-Sep-2022
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Morales-Garzón AGómez-Romero JMartín-Bautista M(2022)Contextual Sentence Embeddings for Obtaining Food Recipe VersionsInformation Processing and Management of Uncertainty in Knowledge-Based Systems10.1007/978-3-031-08974-9_24(306-316)Online publication date: 4-Jul-2022
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