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The Influence of City Size on Dietary Choices and Food Recommendation

Published: 09 July 2017 Publication History

Abstract

Contextual features have been leveraged by recommender systems in many different domains. Traditional contextual features -- such as location and time -- have successfully been combined with collaborative filtering or content-based features. However, it is likely that there are other -- domain-specific -- features that may have even more impact. In this paper, we focus on the influence of city size on food preferences. Apart from location and time, our results show that city size can significantly boost the performance of food recommendation.

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Cited By

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  • (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
  • (2022)A Survey on Healthy Food Decision Influences Through Technological InnovationsACM Transactions on Computing for Healthcare10.1145/34945803:2(1-27)Online publication date: 3-Mar-2022
  • (2021)Increasing Diversity through Dynamic Critique in Conversational Recipe RecommendationsProceedings of the 13th International Workshop on Multimedia for Cooking and Eating Activities10.1145/3463947.3469237(9-16)Online publication date: 21-Aug-2021
  • Show More Cited By

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Published In

cover image ACM Conferences
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization
July 2017
420 pages
ISBN:9781450346351
DOI:10.1145/3079628
  • General Chairs:
  • Maria Bielikova,
  • Eelco Herder,
  • Program Chairs:
  • Federica Cena,
  • Michel Desmarais
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.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 July 2017

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Author Tags

  1. city size differences
  2. food recommendation
  3. online food

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  • Extended-abstract

Funding Sources

  • German Federal Ministry of Education and Research (BMBF)

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UMAP '17
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Acceptance Rates

UMAP '17 Paper Acceptance Rate 29 of 80 submissions, 36%;
Overall Acceptance Rate 162 of 633 submissions, 26%

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UMAP '25

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Cited By

View all
  • (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
  • (2022)A Survey on Healthy Food Decision Influences Through Technological InnovationsACM Transactions on Computing for Healthcare10.1145/34945803:2(1-27)Online publication date: 3-Mar-2022
  • (2021)Increasing Diversity through Dynamic Critique in Conversational Recipe RecommendationsProceedings of the 13th International Workshop on Multimedia for Cooking and Eating Activities10.1145/3463947.3469237(9-16)Online publication date: 21-Aug-2021
  • (2020)Neural Restaurant-aware Dish Recommendation2020 IEEE International Conference on Knowledge Graph (ICKG)10.1109/ICBK50248.2020.00090(599-606)Online publication date: Aug-2020
  • (2019)A Survey on Food ComputingACM Computing Surveys10.1145/332916852:5(1-36)Online publication date: 13-Sep-2019
  • (2017)The Influence of City Size on Dietary ChoicesAdjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization10.1145/3099023.3099058(231-236)Online publication date: 9-Jul-2017
  • (2012)Food Recommender SystemsRecommender Systems Handbook10.1007/978-1-0716-2197-4_23(871-925)Online publication date: 24-Feb-2012

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