research-article

Method for Detecting Near-duplicate Recipe Creators Based on Cooking Instructions and Food Images

Authors:

Masaki Oguni,

Yohei Seki,

Risako Shimada,

Yu HirateAuthors Info & Claims

CEA2017: Proceedings of the 9th Workshop on Multimedia for Cooking and Eating Activities in conjunction with The 2017 International Joint Conference on Artificial Intelligence

Pages 49 - 54

https://doi.org/10.1145/3106668.3106676

Published: 20 August 2017 Publication History

Get Access

Abstract

In this study, we developed a method for detecting near-duplicate recipe creators based on the posting time interval and food images. Some recipe websites contain pairs of recipes with very similar cooking instructions (near-duplicate recipes), which are not useful when searching for recipes. In addition, we found that some recipe creators posted many recipes over a short period of time. Thus, we investigated whether "near-duplicate recipe creators" can be detected by using the cooking instructions and food images as clues. We evaluated our proposed method for near-duplicate recipe creator detection. In terms of the F-score, our method achieved high performance of 0.889 by detecting 19 recipe creators who posted over 30 recipes in one hour, and 0.778 for 46 recipe creators who posted over 20 recipes in one hour.

References

[1]

Jingjing Chen and Chong-Wah Ngo. 2016. Deep-based Ingredient Recognition for Cooking Recipe Retrieval. In Proc. of the 2016 ACM on Multimedia Conf. (MM '16). ACM, Amsterdam, The Netherlands, 32--41.

Digital Library

Google Scholar

[2]

Jacob Cohen. 1960. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20, 1 (1960), 37--46.

Crossref

Google Scholar

[3]

Cristian Grozea, Christian Gehl, and Marius Popescu. 2009. ENCOPLOT: Pairwise Sequence Matching in Linear Time Applied to Plagiarism Detection. In Proc. of the SEPLN '09 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse. San Sebastian (Donostia), Spain, 10--18.

Google Scholar

[4]

Shunsuke Hanai, Hidetsugu Nanba, and Akiyo Nadamoto. 2015. Clustering for Closely Similar Recipes to Extract Spam Recipes in User-generated Recipe Sites. In Proc. of the 17th Int'l Conf. Information Integration and Web-based Applications & Services (iiWAS '15). ACM, Brussels, Belgium, Article 31, 5 pages.

Digital Library

Google Scholar

[5]

Haruka Kubo and Yohei Seki. 2016. Finding Near-duplicate Recipes across Recipe Websites (in Japanese). In Proc. of the 8th Forum on Data Engineering and Information Management 2016 (DEIM '16). Fukuoka, Japan, C8--3.

Google Scholar

[6]

J. Richard Landis and Gary G. Koch. 1977. The Measurement of Observer Agreement for Categorical Data. Biometrics 33, 1 (1977), 159--174.

Crossref

Google Scholar

[7]

Maruha Nichiro Corporation. 2013. Survey on cooking recipes (in Japanese). https://www.maruha-nichiro.co.jp/news_center/research/pdf/20130227_recipe_cyousa.pdf (accessed 2017-04-24). (2013).

Google Scholar

[8]

Yohei Seki and Kouta Ono. 2014. Discriminating Practical Recipes Based on Content Characteristics in Popular Social Recipes. In Proc. of the 2014 ACM Int'l Joint Conf. on Pervasive and Ubiquitous Computing: Adjunct Publication (UbiComp '14 Adjunct). ACM, Seattle, Washington, 487--496.

Digital Library

Google Scholar

[9]

Efstathios Stamatatos. 2009. Intrinsic Plagiarism Detection Using Character n-gram Profiles. In Proc. of the SEPLN '09 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse. San Sebastian (Donostia), Spain, 38--46.

Google Scholar

[10]

Youri van Pinxteren, Gijs Geleijnse, and Paul Kamsteeg. 2011. Deriving a Recipe Similarity Measure for Recommending Healthful Meals. In Proc. of the 16th Int'l Conf. on Intelligent User Interfaces (IUI '11). ACM, Palo Alto, CA, USA, 105--114.

Digital Library

Google Scholar

[11]

Yoko Yamakata, Shinji Imahori, Yuichi Sugiyama, Shinsuke Mori, and Katsumi Tanaka. 2013. Feature Extraction and Summarization of Recipes Using Flow Graph. In Proceedings of the 5th Int'l Conf. on Social Informatics (SocInfo '13) (Lecture Notes in Computer Science), Vol. 8238. Springer-Verlag, Kyoto, Japan, 241--254.

Digital Library

Google Scholar

Cited By

View all

Karikome SSatoh T(2020)Generation and Evaluation of Flow Graphs for Cooking Recipes2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAI-AAI50415.2020.00030(102-109)Online publication date: Sep-2020
https://doi.org/10.1109/IIAI-AAI50415.2020.00030
Ohata TNishihara YYamanishi RIde IYamakata Y(2019)Foods Recommendation System for Meals-out in Nutrient BalanceProceedings of the 11th Workshop on Multimedia for Cooking and Eating Activities10.1145/3326458.3326926(17-23)Online publication date: 5-Jun-2019
https://dl.acm.org/doi/10.1145/3326458.3326926

Recommendations

Calculating Cooking Recipe's Difficulty based on Cooking Activities
CEA2017: Proceedings of the 9th Workshop on Multimedia for Cooking and Eating Activities in conjunction with The 2017 International Joint Conference on Artificial Intelligence

In recent years, there have been plenty of cooking recipes on the internet. Because it has caused difficulty in searching for a cooking recipe that suites one's needs, many studies have researched on how to make these recipes easily accessible. Existing ...
Clustering for closely similar recipes to extract spam recipes in user-generated recipe sites
iiWAS '15: Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services

Nowadays, many user-generated recipe sites are accessible on the internet. On user-generated recipe sites, however, are various spam recipe pages that describe closely similar recipes requiring special cooking equipment, with no preparation ...
Sentence-based and Noise-robust Cross-modal Retrieval on Cooking Recipes and Food Images
ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval

In recent years, people are facing with billions of food images, videos and recipes on social medias. An appropriate technology is highly desired to retrieve accurate contents across food images and cooking recipes, like cross-modal retrieval framework. ...

Comments

Information & Contributors

Information

Published In

CEA2017: Proceedings of the 9th Workshop on Multimedia for Cooking and Eating Activities in conjunction with The 2017 International Joint Conference on Artificial Intelligence

August 2017

64 pages

ISBN:9781450352673

DOI:10.1145/3106668

General Chair:
Ichiro Ide
Nagoya University
,
Program Chair:
Yoko Yamakata
The University of Tokyo

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]

In-Cooperation

The International Joint Conferences on Artificial Intelligence, Inc. (IJCAI)

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

CEA2017

CEA2017: 9th Workshop on Multimedia for Cooking and Eating Activities in conjunction with The 2017 International Joint Conference on Artificial Intelligence

August 20, 2017

Melbourne, Australia

Acceptance Rates

CEA2017 Paper Acceptance Rate 7 of 12 submissions, 58%;

Overall Acceptance Rate 20 of 33 submissions, 61%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
125
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Karikome SSatoh T(2020)Generation and Evaluation of Flow Graphs for Cooking Recipes2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAI-AAI50415.2020.00030(102-109)Online publication date: Sep-2020
https://doi.org/10.1109/IIAI-AAI50415.2020.00030
Ohata TNishihara YYamanishi RIde IYamakata Y(2019)Foods Recommendation System for Meals-out in Nutrient BalanceProceedings of the 11th Workshop on Multimedia for Cooking and Eating Activities10.1145/3326458.3326926(17-23)Online publication date: 5-Jun-2019
https://dl.acm.org/doi/10.1145/3326458.3326926

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.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Recommendations

Calculating Cooking Recipe's Difficulty based on Cooking Activities

Clustering for closely similar recipes to extract spam recipes in user-generated recipe sites

Sentence-based and Noise-robust Cross-modal Retrieval on Cooking Recipes and Food Images

Comments

Information

Published In

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations