Abstract
With the number of people considered to be obese rising across the globe, the role of IT solutions in health management has been receiving increased attention by medical professionals in recent years. This paper focuses on an initial step toward understanding the applicability of recommender techniques in the food and diet domain. By understanding the food preferences and assisting users to plan a healthy and appealing meal, we aim to reduce the effort required of users to change their diet. As an initial feasibility study, we evaluate the performance of collaborative filtering, content-based and hybrid recommender algorithms on a dataset of 43,000 ratings from 512 users. We report on the accuracy and coverage of the algorithms and show that a content-based approach with a simple mechanism that breaks down recipe ratings into ingredient ratings performs best overall.
This research is jointly funded by the Australian Government through the Intelligent Island Program and CSIRO Preventative Health Flagship. The Intelligent Island Program is administered by the Tasmanian Department of Economic Development, Tourism, and the Arts. The authors acknowledge Mealopedia.com and Penguin Group (Australia) for permission to use their data.
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Freyne, J., Berkovsky, S. (2010). Recommending Food: Reasoning on Recipes and Ingredients. In: De Bra, P., Kobsa, A., Chin, D. (eds) User Modeling, Adaptation, and Personalization. UMAP 2010. Lecture Notes in Computer Science, vol 6075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13470-8_36
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DOI: https://doi.org/10.1007/978-3-642-13470-8_36
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