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Link prediction in food heterogeneous graphs for personalised recipe recommendation based on user interactions and dietary restrictions

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Abstract

Recipe data and user interactions and preferences have been widely studied in food computing, especially for the recipe recommendation task. One part of these works seeks to introduce healthy patterns while considering user preferences, known as healthy-aware recommender systems. The major challenge here is to build systems capable of learning the complex structure of recipe data since they involve heterogeneous resources. Internet-sourced recipe collections may also have a representative amount of recipes that do not follow healthy guidelines, thus inhibiting the performance of health-aware recommender systems. We propose a new method for recipe recommendation based on a link prediction algorithm that considers recipes, their healthy features, and users. We train the model twice, once with the whole dataset and once with recipes following healthy guidelines. We follow three strategies for representing recipe data regarding healthy features. In general, training the model in recipe data that follows healthy guidelines achieves better results, especially when representing recipes with numeric nutrition recipe values.

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Notes

  1. Food Standards Agency (FSA): https://www.food.gov.uk/topic/nutrition.

  2. U.S. Food and Drug Administration (FDA Organisation): https://www.fda.gov.

  3. The New Nutrition Facts Label, U.S. Food and Drug Administration (FDA): https://www.fda.gov/food/nutrition-education-resources-materials/new-nutrition-facts-label.

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Acknowledgements

This research was partially supported by the Grant PID2021-123960OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe. It was also funded by “Consejería de Transformación Económica, Industria, Conocimiento y Universidades de la Junta de Andalucía” through a pre-doctoral fellowship program (Grant Ref. PREDOC_00298). In addition, this research has been partially supported by the European Social Fund and the “Consejería de Transformación Económica, Industria, Conocimiento y Universidades de la Junta de Andalucía” through the PAIDI postdoctoral fellowships (Grant Ref. DOC_01451).

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  1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Andrea Morales-Garzón, Karel Gutiérrez-Batista & Maria J. Martin-Bautista

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  1. Andrea Morales-Garzón
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  2. Karel Gutiérrez-Batista
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Correspondence to Andrea Morales-Garzón.

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Morales-Garzón, A., Gutiérrez-Batista, K. & Martin-Bautista, M.J. Link prediction in food heterogeneous graphs for personalised recipe recommendation based on user interactions and dietary restrictions. Computing (2023). https://doi.org/10.1007/s00607-023-01233-2

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