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Recipe recommendations for individual users and groups in a cooking assistance app

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Abstract

Recommender systems are commonly-used tools to assist people in making decisions. However, most research has focused on the domain of recommendations for audio-visual content and e-commerce, whereas the specific characteristics of recommendations for recipes and cooking did not receive enough attention. Since meals are often consumed in group (with friends or family), there is a need for group recommendations, taking into account the preferences of all group members. Also cuisine, allergies, disliked ingredients, diets, dish type, and required time to prepare are important factors for recipe selection. For 13 algorithms, we evaluated the recommendations for individuals and for groups using a dataset of recipe ratings. The best algorithm and a baseline algorithm based on popularity were selected for our mobile kitchen experience and recipe application, which assists users in the cooking process and provides recipe recommendations. Although significant differences between both algorithms were witnessed in the offline evaluation with the dataset, the differences were less noticeable in the online evaluation with real users. Because of the cold-start problem, the advanced algorithm failed to reach its full accuracy potential, but excelled in other quality features such as diversity, perceived usefulness, and confidence. We also witnessed a better evaluation (about half a star) of the recommendations by the more advanced cooks.

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Availability of data and materials

The data that support the findings of this study are available from FoodPairing.com but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available

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Acknowledgements

This study was partially based on the results of the PhD dissertation of the first author, Toon De Pessemier, more specifically on the fourth chapter that handles group recommendations [12]. The work was executed within the imec.icon project IoT Chef, a research project bringing together academic researchers and industry partners. The IoT Chef project was co-financed by imec and received project support from Flanders Innovation & Entrepreneurship

Funding

This work was co-financed by imec and received financial support from Flanders Innovation & Entrepreneurship. Grand number HBC.2017.0625

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Authors and Affiliations

  1. Information technology, imec-WAVES-UGent, iGent - Technologiepark 126, Ghent, 9052, Oost-Vlaanderen, Belgium

    Toon De Pessemier, Kris Vanhecke, Luc Martens, Wout Joseph & David Plets

  2. Communication sciences, imec-MICT-UGent, Miriam Makebaplein 1, Ghent, 9000, Oost-Vlaanderen, Belgium

    Anissa All, Stephanie Van Hove & Lieven De Marez

Authors
  1. Toon De Pessemier
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  2. Kris Vanhecke
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Contributions

All authors contributed to the study conception and design. The first draft of the manuscript was written by Toon De Pessemier and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript

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Correspondence to Toon De Pessemier.

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The authors declare that there are no conflicts of interest regarding the publication of this article. The authors have no relevant financial or non-financial interests to disclose. The authors have no competing interests to declare that are relevant to the content of this article

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De Pessemier, T., Vanhecke, K., All, A. et al. Recipe recommendations for individual users and groups in a cooking assistance app. Appl Intell 53, 27027–27043 (2023). https://doi.org/10.1007/s10489-023-04909-6

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