short-paper

Understanding and Predicting Online Food Recipe Production Patterns

Authors:

Tomasz Kusmierczyk,

Christoph Trattner,

Kjetil NørvågAuthors Info & Claims

HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media

Pages 243 - 248

https://doi.org/10.1145/2914586.2914632

Published: 10 July 2016 Publication History

Abstract

Studying online food patterns has recently become an active field of research. While there are a growing body of studies that investigate how online food in consumed, little effort has been devoted yet to understand how online food recipes are being created. To contribute to this lack of knowledge in the area, we present in this paper the results of a large-scale study that aims at understanding how historical, social and temporal factors impact on the online food creation process. Several experiments reveal the extent to which various factors are useful in predicting future recipe production.

References

[1]

S. Abbar, Y. Mejova, and I. Weber. You tweet what you eat: Studying food consumption through twitter. In Proc. of CHI'15, 2015.

Digital Library

[2]

Y.-Y. Ahn, S. E. Ahnert, J. P. Bagrow, and A.-L. Barabási. Flavor network and the principles of food pairing. Scientific reports, 1, 2011.

[3]

S. Berkovsky and J. Freyne. Group-based recipe recommendations: Analysis of data aggregation strategies. In Proc. of RecSys '10, pages 111--118, 2010.

Digital Library

[4]

P. G. Campos, F. Dıez, and I. Cantador. Time-aware recommender systems: A comprehensive survey and analysis of existing evaluation protocols. UMUAI, 24(1--2):67--119, 2014.

Digital Library

[5]

M. De Choudhury and S. S. Sharma. Characterizing dietary choices, nutrition, and language in food deserts via social media. In Proc. of CSCW '16, 2016.

Digital Library

[6]

D. Elsweiler and M. Harvey. Towards automatic meal plan recommendations for balanced nutrition. In Proc. of RecSys '15, pages 313--316, 2015.

Digital Library

[7]

J. Freyne and S. Berkovsky. Intelligent food planning: Personalized recipe recommendation. In Proc. of IUI '10, pages 321--324, 2010.

Digital Library

[8]

Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. MyMediaLite: a free recommender system library. In Proc. of RecSys '11, pages 305--308, 2011.

Digital Library

[9]

M. Ge, F. Ricci, and D. Massimo. Health-aware food recommender system. In Proc. of RecSys '15, pages 333--334, 2015.

Digital Library

[10]

T. Kusmierczyk, C. Trattner, and K. Nørvåg. Temporal patterns in online food innovation. In Proc. of WWW '15 Companion, pages 1345--1350, 2015.

Digital Library

[11]

T. Kusmierczyk, C. Trattner, and K. Nørvåg. Temporality in online food recipe consumption and production. In Proc. of WWW'15, 2015.

Digital Library

[12]

M. Rokicki, E. Herder, and E. Demidova. What's on my plate: Towards recommending recipe variations for diabetes patients. In Posters, Demos, Late-breaking Results and Workshop Proc. of UMAP'15, 2015.

[13]

A. Said and A. Bellogın. You are what you eat! tracking health through recipe interactions. In Proc. of RSWeb'14, 2014.

[14]

C.-Y. Teng, Y.-R. Lin, and L. A. Adamic. Recipe recommendation using ingredient networks. In Proc. of WebSci'12, 2012.

Digital Library

[15]

C. Trattner, T. Kusmierczyk, and K. Nørvåg. FOODWEB - studying food consumption and production patterns on the web. ERCIM News, 2016(104), 2016.

[16]

M. Trevisiol, L. Chiarandini, and R. A. Baeza-Yates. Buon appetito: recommending personalized menus. In Proc. of HT'14, pages 327--329, 2014.

Digital Library

[17]

C. Wagner, P. Singer, and M. Strohmaier. The nature and evolution of online food preferences. EPJ Data Science, 3(1):1--22, 2014.

[18]

R. West, R. W. White, and E. Horvitz. From cookies to cooks: Insights on dietary patterns via analysis of web usage logs. In Proc. of WWW'13, 2013.

Digital Library

Cited By

Pagou EKamla VTchappi INgathic JTsakam LNajjar A(2024)Food Recommender System in Sub-Saharan Africa: Challenges and ProspectsSafe, Secure, Ethical, Responsible Technologies and Emerging Applications10.1007/978-3-031-56396-6_17(276-287)Online publication date: 18-Apr-2024
https://doi.org/10.1007/978-3-031-56396-6_17
Zhang QElsweiler DTrattner C(2023)Understanding and predicting cross-cultural food preferences with online recipe imagesInformation Processing & Management10.1016/j.ipm.2023.10344360:5(103443)Online publication date: Sep-2023
https://doi.org/10.1016/j.ipm.2023.103443
Bogina VKuflik TJannach DBielikova MKompan MTrattner C(2022)Considering temporal aspects in recommender systems: a surveyUser Modeling and User-Adapted Interaction10.1007/s11257-022-09335-w33:1(81-119)Online publication date: 4-Jul-2022
https://doi.org/10.1007/s11257-022-09335-w
Show More Cited By

Index Terms

Understanding and Predicting Online Food Recipe Production Patterns
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval

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Published In

cover image ACM Conferences

HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media

July 2016

354 pages

ISBN:9781450342476

DOI:10.1145/2914586

General Chairs:
Jamie Blustein
Dalhousie University, Canada
,
Eelco Herder
L3S Research Center, Germany
,
Program Chairs:
Jessica Rubart
OWL University of Applied Sciences, Germany
,
Helen Ashman
University of South Australia

Copyright © 2016 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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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

In-Cooperation

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2016

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HT '16

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SIGWEB

HT '16: 27th ACM Conference on Hypertext and Social Media

July 10 - 13, 2016

Nova Scotia, Halifax, Canada

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HT '16 Paper Acceptance Rate 16 of 54 submissions, 30%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

Pagou EKamla VTchappi INgathic JTsakam LNajjar A(2024)Food Recommender System in Sub-Saharan Africa: Challenges and ProspectsSafe, Secure, Ethical, Responsible Technologies and Emerging Applications10.1007/978-3-031-56396-6_17(276-287)Online publication date: 18-Apr-2024
https://doi.org/10.1007/978-3-031-56396-6_17
Zhang QElsweiler DTrattner C(2023)Understanding and predicting cross-cultural food preferences with online recipe imagesInformation Processing & Management10.1016/j.ipm.2023.10344360:5(103443)Online publication date: Sep-2023
https://doi.org/10.1016/j.ipm.2023.103443
Bogina VKuflik TJannach DBielikova MKompan MTrattner C(2022)Considering temporal aspects in recommender systems: a surveyUser Modeling and User-Adapted Interaction10.1007/s11257-022-09335-w33:1(81-119)Online publication date: 4-Jul-2022
https://doi.org/10.1007/s11257-022-09335-w
Tian YZhang CMetoyer RChawla NDemartini GZuccon GCulpepper JHuang ZTong H(2021)Recipe Representation Learning with NetworksProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482468(1824-1833)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482468
Zhang QElsweiler DTrattner C(2020)Visual Cultural Biases in Food ClassificationFoods10.3390/foods90608239:6(823)Online publication date: 23-Jun-2020
https://doi.org/10.3390/foods9060823
Marin JBiswas AOfli FHynes NSalvador AAytar YWeber ITorralba A(2020)Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food ImagesIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2019.2927476(1-1)Online publication date: 2020
https://doi.org/10.1109/TPAMI.2019.2927476
Aiello LQuercia DSchifanella RDel Prete L(2020)Tesco Grocery 1.0, a large-scale dataset of grocery purchases in LondonScientific Data10.1038/s41597-020-0397-77:1Online publication date: 18-Feb-2020
https://doi.org/10.1038/s41597-020-0397-7
Mujtaba DMahapatra N(2019)Modeling the Automation Level of Cyber-Physical Systems Designed for Food Preparation2019 9th International Symposium on Embedded Computing and System Design (ISED)10.1109/ISED48680.2019.9096241(1-5)Online publication date: Dec-2019
https://doi.org/10.1109/ISED48680.2019.9096241
Carvalho MCadène RPicard DSoulier LThome NCord MCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Cross-Modal Retrieval in the Cooking ContextThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210036(35-44)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210036
Trattner CMoesslang DElsweiler D(2018)On the predictability of the popularity of online recipesEPJ Data Science10.1140/epjds/s13688-018-0149-57:1Online publication date: 5-Jul-2018
https://doi.org/10.1140/epjds/s13688-018-0149-5
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