Abstract
Food and diet are complex domains for recommender technology, but the need for systems that assist users in embarking on and engaging with healthy living programs has never been more real. One key to sustaining long term engagement with eHealth services is the provision of tools, which assist and train users in planning correctly around the areas of diet and exercise. These tools require an understanding of user reasoning as well as user needs and are ideal application areas for recommender and personalization technologies. Here, we report on a large scale analysis of real user ratings on a set of recipes in order to judge the applicability and practicality of a number of personalization algorithms. Further to this, we report on apparent user reasoning patterns uncovered in rating data supplied for recipes and suggest ways to exploit this reasoning understanding in the recommendation process.
This research is jointly funded by the Australian Government through the Intelligent Island Program and CSIRO Food and Nutritional Sciences. 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., Smith, G. (2011). Recipe Recommendation: Accuracy and Reasoning. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds) User Modeling, Adaption and Personalization. UMAP 2011. Lecture Notes in Computer Science, vol 6787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22362-4_9
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DOI: https://doi.org/10.1007/978-3-642-22362-4_9
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