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Emotional Insights for Food Recommendations

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Advances in Information Retrieval (ECIR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14609))

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Abstract

Food recommendation systems have become pivotal in offering personalized suggestions, enabling users to discover recipes in line with their tastes. However, despite the existence of numerous such systems, there are still unresolved challenges. Much of the previous research predominantly lies on users’ past preferences, neglecting the significant aspect of discerning users’ emotional insights. Our framework aims to bridge this gap by pioneering emotion-aware food recommendation. The study strives for enhanced accuracy by delivering recommendations tailored to a broad spectrum of emotional and dietary behaviors. Uniquely, we introduce five novel scores for Influencer-Followers, Visual Motivation, Adventurous, Health and Niche to gauge a user’s inclination toward specific emotional insights. Subsequently, these indices are used to re-rank the preliminary recommendation, placing a heightened focus on the user’s emotional disposition. Experimental results on a real-world food social network dataset reveal that our system outperforms alternative emotion-unaware recommender systems, yielding an average performance boost of roughly 6%. Furthermore, the results reveal a rise of over 30% in accuracy metrics for some users exhibiting particular emotional insights.

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Acknowledgements

The project is supported by the Research Council of Finland (former Academy of Finland) and Profi5 DigiHealth Research Program (project number 326291).

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Authors and Affiliations

  1. University of Oulu, Center for Machine Vision and Signal Analysis, Oulu, Finland

    Mehrdad Rostami & Mourad Oussalah

  2. University of Amsterdam, Information Retrieval Lab, Amsterdam, Netherlands

    Ali Vardasbi & Mohammad Aliannejadi

Authors
  1. Mehrdad Rostami
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  2. Ali Vardasbi
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  3. Mohammad Aliannejadi
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  4. Mourad Oussalah
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Corresponding author

Correspondence to Mehrdad Rostami .

Editor information

Editors and Affiliations

  1. Georgetown University, Washington, WA, USA

    Nazli Goharian

  2. University of Pisa, PISA, Pisa, Italy

    Nicola Tonellotto

  3. King's College London, London, UK

    Yulan He

  4. University College London, London, UK

    Aldo Lipani

  5. University of Glasgow, Glasgow, UK

    Graham McDonald

  6. University of Glasgow, Glasgow, UK

    Craig Macdonald

  7. University of Glasgow, Glasgow, UK

    Iadh Ounis

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Rostami, M., Vardasbi, A., Aliannejadi, M., Oussalah, M. (2024). Emotional Insights for Food Recommendations. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14609. Springer, Cham. https://doi.org/10.1007/978-3-031-56060-6_16

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  • DOI: https://doi.org/10.1007/978-3-031-56060-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56059-0

  • Online ISBN: 978-3-031-56060-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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