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Event Mining Driven Context-Aware Personal Food Preference Modelling

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Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12665))

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Abstract

A personal food model (PFM) is essential for high-quality food recommendation systems to enhance health and enjoyment. We can build such models using food logging platforms that capture the users’ food events. As proposed in the Westermann and Jain event model, capturing six facets of multi-modal data provides a holistic view of any event. Five of these facets are captured during the event (temporal, structural, informational, experiential, spatial), while the sixth facet is related to the causality of the event. This causal facet is needed to build a robust PFM if all the other relevant information in the aforementioned five facets are captured. Any food logger and subsequent processing should collect all this data in the food event. Ultimately, we want to know what caused this person to eat this food and what changes this food event causes in the person’s health state. In this paper, we identify details of the food event model that may help build a causal understanding in PFM to address the first aspect of the causality, what may be the contextual factors that cause a certain food event to occur for a user. We utilize an event mining approach to determine the causal relationships to build a contextual understanding of the PFM. We generate data using a food event simulator that can generate needed food event data for a person with known PFM. The event mining results uncover this hidden PFM and demonstrate the greater efficacy of this approach than a traditionally designed PFM.

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Authors and Affiliations

  1. University of California, Irvine, CA, USA

    Vaibhav Pandey, Ali Rostami, Nitish Nag & Ramesh Jain

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  1. Vaibhav Pandey
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  4. Ramesh Jain
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Corresponding author

Correspondence to Vaibhav Pandey .

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Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Pandey, V., Rostami, A., Nag, N., Jain, R. (2021). Event Mining Driven Context-Aware Personal Food Preference Modelling. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_52

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  • DOI: https://doi.org/10.1007/978-3-030-68821-9_52

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