skip to main content
10.1145/3640457.3688193acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
short-paper

Recommending Healthy and Sustainable Meals exploiting Food Retrieval and Large Language Models

Published: 08 October 2024 Publication History

Abstract

Given the rising global concerns about healthy nutrition and environmental sustainability, individuals need more and more support in making good choices concerning their daily meals. To this end, in this paper we introduce HeaSE, a framework for Healthy And Sustainable Eating. Given an input recipe, HeaSE identifies healthier and more sustainable meals by exploiting retrieval techniques and large language models. The framework works in two steps. First, it uses food retrieval strategies based on macro-nutrient information to identify candidate alternative meals. This ensures that the substitutions maintain a similar nutritional profile. Next, HeaSE employs large language models to re-rank these potential replacements while considering factors beyond just nutrition, such as the recipe’s environmental impact. In the experimental evaluation, we showed the capabilities of LLMs in identifying more sustainable and healthier alternatives within a set of candidate options. This highlights the potential of these models to guide users towards food choices that are both nutritious and environmentally responsible.

References

[1]
Felix Bölz, Diana Nurbakova, Sylvie Calabretto, Armin Gerl, Lionel Brunie, and Harald Kosch. 2023. HUMMUS: A Linked, Healthiness-Aware, User-centered and Argument-Enabling Recipe Data Set for Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems (Singapore, Singapore) (RecSys ’23). Association for Computing Machinery, New York, NY, USA, 1–11. https://doi.org/10.1145/3604915.3609491
[2]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.
[3]
Ayoub El Majjodi, Alain D Starke, and Christoph Trattner. 2022. Nudging towards health? examining the merits of nutrition labels and personalization in a recipe recommender system. In Proceedings of the 30th ACM conference on user modeling, adaptation and personalization. 48–56.
[4]
David Elsweiler, Morgan Harvey, Bernd Ludwig, and Alan Said. 2015. Bringing the "healthy" into Food Recommenders. In International Workshop on Decision Making and Recommender Systems. https://api.semanticscholar.org/CorpusID:1838398
[5]
Ignazio Gallo, Nicola Landro, Riccardo La Grassa, and Andrea Turconi. 2022. Food Recommendations for Reducing Water Footprint. Sustainability 14, 7 (2022). https://doi.org/10.3390/su14073833
[6]
Mouzhi Ge, Francesco Ricci, and David Massimo. 2015. Health-aware Food Recommender System. In Proceedings of the 9th ACM Conference on Recommender Systems (Vienna, Austria) (RecSys ’15). Association for Computing Machinery, New York, NY, USA, 333–334. https://doi.org/10.1145/2792838.2796554
[7]
Christina Hartmann, Gianna Lazzarini, Angela Funk, and Michael Siegrist. 2021. Measuring consumers’ knowledge of the environmental impact of foods. Appetite 167 (2021), 105622.
[8]
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33 (2020), 9459–9474.
[9]
World Health Organization. 2020. Healthy diet. https://www.who.int/news-room/fact-sheets/detail/healthy-diet.
[10]
Divya Pandey, Madhoolika Agrawal, and Jai Shanker Pandey. 2011. Carbon footprint: current methods of estimation. Environmental monitoring and assessment 178 (2011), 135–160.
[11]
Tashina Petersson, Luca Secondi, Andrea Magnani, Marta Antonelli, Katarzyna Dembska, Riccardo Valentini, Alessandra Varotto, and Simona Castaldi. 2021. A multilevel carbon and water footprint dataset of food commodities. Scientific data 8, 1 (2021), 127.
[12]
Irtiqa Shabir, Kshirod Kumar Dash, Aamir Hussain Dar, Vinay Kumar Pandey, Ufaq Fayaz, Shivangi Srivastava, and Nisha R. 2023. Carbon footprints evaluation for sustainable food processing system development: A comprehensive review. Future Foods 7 (2023), 100215. https://doi.org/10.1016/j.fufo.2023.100215
[13]
Chun-Yuen Teng, Yu-Ru Lin, and Lada A Adamic. 2012. Recipe recommendation using ingredient networks. In Proceedings of the 4th annual ACM web science conference. 298–307.
[14]
Christoph Trattner and David Elsweiler. 2017. Food recommender systems: important contributions, challenges and future research directions. arXiv preprint arXiv:1711.02760 (2017).
[15]
Christoph Trattner and David Elsweiler. 2017. Investigating the healthiness of internet-sourced recipes: implications for meal planning and recommender systems. In Proceedings of the 26th international conference on world wide web. 489–498.
[16]
Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Qi Liu, Tianyu Liu, and Zhifang Sui. 2023. Large Language Models are not Fair Evaluators. arxiv:2305.17926 [cs.CL]

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
October 2024
1438 pages
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 the author(s) 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: 08 October 2024

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Food Recommendation
  2. Health-aware Recommender Systems
  3. Large Language Models
  4. Sustainability

Qualifiers

  • Short-paper
  • Research
  • Refereed limited

Conference

Acceptance Rates

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 196
    Total Downloads
  • Downloads (Last 12 months)196
  • Downloads (Last 6 weeks)30
Reflects downloads up to 18 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media