Zhao, M., Harvey, M. orcid.org/0000-0001-5504-2089, Cameron, D. et al. (1 more author) (2024) The effect of simulated contextual factors on recipe rating and nutritional intake behaviour. In: Clough, P., Harvey, M. and Hopfgartner, F., (eds.) CHIIR '24: Proceedings of the 2024 Conference on Human Information Interaction and Retrieval. ACM SIGIR Conference on Human Information Interaction And Retrieval, 10-14 Mar 2024, Sheffield, United Kingdom. Association for Computing Machinery , pp. 97-107. ISBN 9798400704345
Abstract
Despite the importance of context in Recommender Systems (RSs) more generally, and its clear applicability in the food domain, most existing research focuses on single contextual factors, and only considers simple extrinsic factors such as location and time. No RSs research has systematically explored the impact of multiple dynamic factors, or investigated the effect of emotion in determin-ing people’s eating, recipe rating and nutritional intake behaviour. To bridge these gaps, we conducted a comprehensive large-scale (n=397) crowdsourced experimental study to uncover the intri-cate relationship between various simulated contextual factors and users’ subsequent recipe rating and implied nutritional intake be-haviour. We further aimed to explore how these contextual fac-tors can be incorporated to improve recommendation performance. Four distinct types of contextual factors were investigated: seasonal, emotional, busyness and physical activity, encompassing a total of seven elements. Our findings show that people’s eating prefer-ences and the likelihood of them choosing to eat healthy recipes vary depending on the simulated context they find themselves in. Moreover, we demonstrate how these contextual features can be used to significantly improve recipe rating prediction performance. Our research has implications for the future development of food RSs, and shows that emotion-aware systems could lead to better healthy food recommendations.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. Except as otherwise noted, this author-accepted version of a conference proceeding published in CHIIR '24: Proceedings of the 2024 Conference on Human Information Interaction and Retrieval is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Contextual features; Machine learning; Context-aware food recommender systems; Healthy recommendations; User study |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Jan 2024 10:17 |
Last Modified: | 28 Mar 2024 13:00 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Refereed: | Yes |
Identification Number: | 10.1145/3627508.3638328 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:207238 |