research-article

“Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface

Authors:

Christoph TrattnerAuthors Info & Claims

RecSys '21: Proceedings of the 15th ACM Conference on Recommender Systems

Pages 124 - 132

https://doi.org/10.1145/3460231.3474232

Published: 13 September 2021 Publication History

Abstract

Food recommender systems optimize towards a user’s current preferences. However, appetites may vary, in the sense that users might seek healthy recipes today and look for unhealthy meals tomorrow. In this paper, we propose a novel approach in the food domain to diversify recommendations across different lists to ‘serve’ different users goals, compiled in a multi-list food recommender interface. We evaluated our interface in a 2 (single list vs multiple lists) x 2 (without or with explanations) between-subject user study (N = 366), linking choice behavior and evaluation aspects through the user experience framework. Our multi-list interface was evaluated more favorably than a single-list interface, in terms of diversity and choice satisfaction. Moreover, it triggered changes in food choices, even though these choices were less healthy than those made in the single-list interface.

Supplementary Material

MP4 File (Starke2021.mp4)

Presentation Video for our paper on Multi-list Food Recommender Interfaces

Download
20.43 MB

References

[1]

Yuki M Asano and Gesa Biermann. 2019. Rising adoption and retention of meat-free diets in online recipe data. Nature Sustainability 2, 7 (2019), 621–627.

[2]

James R. Bettman, Mary Frances Luce, and John W. Payne. 1998. Constructive Consumer Choice Processes. Journal of Consumer Research 25, 3 (Dec 1998), 187–217.

[3]

Dirk Bollen, Bart P Knijnenburg, Martijn C Willemsen, and Mark Graus. 2010. Understanding choice overload in recommender systems. In Proceedings of the fourth ACM conference on Recommender systems. ACM, New York, NY, USA, 63–70.

Digital Library

[4]

Li Chen and Pearl Pu. 2010. Eye-tracking study of user behavior in recommender interfaces. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6075 LNCS, June 2010(2010), 375–380.

[5]

Li Chen and Pearl Pu. 2012. Critiquing-based recommenders: survey and emerging trends. User Modeling and User-Adapted Interaction 22, 1 (2012), 125–150.

Digital Library

[6]

Alexander Chernev, Ulf Böckenholt, and Joseph Goodman. 2015. Choice overload: A conceptual review and meta-analysis. Journal of Consumer Psychology 25, 2 (2015), 333–358.

[7]

David Elsweiler, Christoph Trattner, and Morgan Harvey. 2017. Exploiting food choice biases for healthier recipe recommendation. In Proceedings of the 40th international acm sigir conference on research and development in information retrieval. ACM, New York, NY, USA, 575–584.

Digital Library

[8]

Jill Freyne and Shlomo Berkovsky. 2010. Intelligent food planning: personalized recipe recommendation. In Proceedings of the 15th international conference on Intelligent user interfaces. ACM, New York, NY, USA, 321–324.

Digital Library

[9]

Mouzhi Ge, Carla Delgado-Battenfeld, and Dietmar Jannach. 2010. Beyond accuracy: evaluating recommender systems by coverage and serendipity. In Proceedings of the fourth ACM conference on Recommender systems. ACM, ACM, New York, NY, USA, 257–260.

Digital Library

[10]

Carlos A Gomez-Uribe and Neil Hunt. 2015. The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS) 6, 4(2015), 1–19.

Digital Library

[11]

Rong Hu and Pearl Pu. 2011. Enhancing recommendation diversity with organization interfaces. In Proceedings of the 16th international conference on Intelligent user interfaces. ACM, New York, NY, USA, 347–350.

Digital Library

[12]

J.Z. Ilich, J.A. Vollono, and R.A. Brownbill. 1999. Impact of Nutritional Knowledge on Food Choices and Dietary Intake of College Students. Journal of the American Dietetic Association 99, 9, Supplement(1999), A89.

[13]

Sheena S Iyengar and Mark R Lepper. 2000. When choice is demotivating: Can one desire too much of a good thing?Journal of personality and social psychology 79, 6(2000), 995.

[14]

Dietmar Jannach, Mathias Jesse, Michael Jugovac, and Christoph Trattner. 2021. Exploring Multi-List User Interfaces for Similar-Item Recommendations. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. ACM, New York, NY, USA, 224–228.

Digital Library

[15]

Rex B Kline. 2015. Principles and practice of structural equation modeling. Guilford publications, New York, NY, USA.

[16]

Bart P. Knijnenburg and Martijn C. Willemsen. 2015. Evaluating recommender systems with user experiments. Springer, New York, NY, USA, 309–352.

[17]

Bart P. Knijnenburg, Martijn C. Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction 22, 4–5 (2012), 441–504.

Digital Library

[18]

Ricardo Gomes Lage, Frederico Durao, Peter Dolog, and Avaré Stewart. 2011. Applicability of recommender systems to medical surveillance systems. In Proceedings of the second international workshop on Web science and information exchange in the medical web. ACM, New York, NY, USA, 1–6.

Digital Library

[19]

Cataldo Musto, Christoph Trattner, Alain Starke, and Giovanni Semeraro. 2020. Towards a knowledge-aware food recommender system exploiting holistic user models. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. ACM, New York, NY, USA, 333–337.

Digital Library

[20]

Theodora Nanou, George Lekakos, and Konstantinos Fouskas. 2010. The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system. Multimedia Systems 16, 4-5 (2010), 219–230.

Digital Library

[21]

Department of Health UK and Food Standards Agency. 2016. Guide to creating a front of pack (FoP) nutrition label for pre-packed products sold through retail outlets. (2016). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/566251/FoP_Nutrition_labelling_UK_guidance.pdf

[22]

Florian Pecune, Lucile Callebert, and Stacy Marsella. 2020. A Recommender System for Healthy and Personalized Recipes Recommendations. In HealthRecSys@ RecSys. ACM, New York, NY, USA, 15–20.

[23]

Bartosz Porebski, Karol Przystalski, and Leszek Nowak. 2011. Building PHP Applications with Symfony, CakePHP, and Zend Framework. John Wiley and Sons, Indianapolis, IN, USA.

[24]

Pearl Pu and Li Chen. 2007. Trust-inspiring explanation interfaces for recommender systems. Knowledge-Based Systems 20, 6 (2007), 542–556.

Digital Library

[25]

Pearl Pu, Li Chen, and Rong Hu. 2011. A user-centric evaluation framework for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems. ACM, New York, NY, USA, 157–164.

Digital Library

[26]

Hanna Schäfer, Santiago Hors-Fraile, Raghav Pavan Karumur, André Calero Valdez, Alan Said, Helma Torkamaan, Tom Ulmer, and Christoph Trattner. 2017. Towards health (aware) recommender systems. In Proceedings of the 2017 international conference on digital health. ACM, New York, NY, USA, 157–161.

Digital Library

[27]

Benjamin Scheibehenne, Rainer Greifeneder, and Peter M Todd. 2010. Can there ever be too many options? A meta-analytic review of choice overload. Journal of consumer research 37, 3 (2010), 409–425.

[28]

Alain Starke. 2019. RecSys Challenges in achieving sustainable eating habits. In HealthRecSys@RecSys. CEUR-WS, Aachen, DE, 29–30.

[29]

Alain Starke, Martijn Willemsen, and Chris Snijders. 2017. Effective User Interface Designs to Increase Energy-efficient Behavior in a Rasch-based Energy Recommender System. In Proceedings of the Eleventh ACM Conference on Recommender Systems(RecSys ’17). ACM, New York, NY, USA, 65–73. https://doi.org/10.1145/3109859.3109902

Digital Library

[30]

Alain D Starke, Elias Kløverød Kløverød Brynestad, Sveinung Hauge, and Louise Sandal Løkeland. 2021. Nudging Healthy Choices in Food Search Through List Re-Ranking. In Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. ACM, New York, NY, USA, 293–298.

[31]

Alain D. Starke and Christoph Trattner. 2021. Promoting Healthy Food Choices Online: A Case for Multi-List Recommender Systems. In HEALTHI’21: Joint Proceedings of ACM IUI 2021 Workshops. CEUR-WS, Aachen, DE, 3 pages.

[32]

Alain D Starke, Martijn C Willemsen, and Chris Snijders. 2020. With a little help from my peers: Depicting social norms in a recommender interface to promote energy conservation. In Proceedings of the 25th International Conference on Intelligent User Interfaces. ACM, New York, NY, USA, 568–578.

Digital Library

[33]

Alain D Starke, Martijn C Willemsen, and Christoph Trattner. 2021. Nudging Healthy Choices in Food Search Through Visual Attractiveness. Frontiers in Artificial Intelligence 4 (2021), 20.

[34]

Nava Tintarev and Judith Masthoff. 2012. Evaluating the Effectiveness of Explanations for Recommender Systems. User Modeling and User-Adapted Interaction 22, 4–5 (Oct 2012), 399–439.

Digital Library

[35]

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. ACM, New York, NY, USA, 489–498.

Digital Library

[36]

Christoph Trattner and David Elsweiler. 2019. Food Recommendations. In Collaborative recommendations: Algorithms, practical challenges and applications. World Scientific, 653–685.

[37]

Christoph Trattner, David Elsweiler, and Simon Howard. 2017. Estimating the healthiness of internet recipes: a cross-sectional study. Frontiers in public health 5 (2017), 16.

[38]

Christoph Trattner and Dietmar Jannach. 2020. Learning to recommend similar items from human judgments. User Modeling and User-Adapted Interaction 30, 1 (March 2020), 1–49.

[39]

Christoph Trattner, Dominik Moesslang, and David Elsweiler. 2018. On the predictability of the popularity of online recipes. EPJ Data Science 7, 1 (2018), 1–39.

Cited By

De Croon RSegovia-Lizano DFinglas PVanden Abeele VVerbert K(2025)An Explanation Interface for Healthy Food Recommendations in a Real-Life Workplace Deployment: User-Centered Design StudyJMIR mHealth and uHealth10.2196/5127113(e51271-e51271)Online publication date: 11-Feb-2025
https://doi.org/10.2196/51271
Peška LSixtová IHoksza DBernhauer DLokoč JSkopal T(2025)Unified Visual-Aware Representations for Data AnalyticsIEEE Access10.1109/ACCESS.2025.353433013(19694-19715)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3534330
Jannach DZanker M(2024)A Survey on Intent-aware Recommender SystemsACM Transactions on Recommender Systems10.1145/37008903:2(1-32)Online publication date: 16-Oct-2024
https://dl.acm.org/doi/10.1145/3700890
Show More Cited By

Recommendations

Examining the User Evaluation of Multi-List Recommender Interfaces in the Context of Healthy Recipe Choices
Multi-list recommender systems have become widespread in entertainment and e-commerce applications. Yet, extensive user evaluation research is missing. Since most content is optimized toward a user’s current preferences, this may be problematic in ...
“Tell Me Why”: using natural language justifications in a recipe recommender system to support healthier food choices
Abstract
Users of online recipe websites tend to prefer unhealthy foods. Their popularity undermines the healthiness of traditional food recommender systems, as many users lack nutritional knowledge to make informed food decisions. Moreover, the presented ...
Online Recommender System for Personalized Nutrition Advice
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

The general recommendations for addressing non-communicable diseases, which are responsible for two thirds of deaths globally, are mainly related to lifestyle changes, such as diet and physical activity. Challenges with encouraging healthy diets include ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

RecSys '21: Proceedings of the 15th ACM Conference on Recommender Systems

September 2021

883 pages

ISBN:9781450384582

DOI:10.1145/3460231

Copyright © 2021 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 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: 13 September 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Research Council of Norway

Conference

RecSys '21

Sponsor:

RecSys '21: Fifteenth ACM Conference on Recommender Systems

September 27 - October 1, 2021

Amsterdam, Netherlands

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
1,027
Total Downloads

Downloads (Last 12 months)68
Downloads (Last 6 weeks)13

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

De Croon RSegovia-Lizano DFinglas PVanden Abeele VVerbert K(2025)An Explanation Interface for Healthy Food Recommendations in a Real-Life Workplace Deployment: User-Centered Design StudyJMIR mHealth and uHealth10.2196/5127113(e51271-e51271)Online publication date: 11-Feb-2025
https://doi.org/10.2196/51271
Peška LSixtová IHoksza DBernhauer DLokoč JSkopal T(2025)Unified Visual-Aware Representations for Data AnalyticsIEEE Access10.1109/ACCESS.2025.353433013(19694-19715)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3534330
Jannach DZanker M(2024)A Survey on Intent-aware Recommender SystemsACM Transactions on Recommender Systems10.1145/37008903:2(1-32)Online publication date: 16-Oct-2024
https://dl.acm.org/doi/10.1145/3700890
Jeng J(2024)Bridging Viewpoints in News with Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688008(1283-1289)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688008
Starke ABremnes AKnudsen ETrilling DTrattner CBoratto LGena CMarras MGermanakos PPopescus E(2024)Perception versus Reality: Evaluating User Awareness of Political Selective Exposure in News Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665189(286-291)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665189
Geninatti Cossatin ABoratto LGena CMarras MGermanakos PPopescus E(2024)Promoting Green Fashion Consumption in Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664922(50-54)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664922
Starke AWillemsen M(2024)Psychologically Informed Design of Energy Recommender Systems: Are Nudges Still Effective in Tailored Choice Environments?A Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_9(221-259)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_9
Loepp B(2023)Multi-list interfaces for recommender systems: survey and future directionsFrontiers in Big Data10.3389/fdata.2023.12397056Online publication date: 10-Aug-2023
https://doi.org/10.3389/fdata.2023.1239705
Loepp BZiegler J(2023)How Users Ride the Carousel: Exploring the Design of Multi-List Recommender Interfaces From a User PerspectiveProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3610638(1090-1095)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3610638
Liang YPonnada ALamere PDaskalova N(2023)Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender SystemsProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584032(142-155)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584032
Show More Cited By

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.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten