Recipe Recommendation: Accuracy and Reasoning

  • Jill Freyne
  • Shlomo Berkovsky
  • Gregory Smith
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)


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.


Collaborative filtering content-based machine learning recipes personalization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jill Freyne
    • 1
  • Shlomo Berkovsky
    • 1
  • Gregory Smith
    • 1
  1. 1.Tasmanian ICT Center, CSIROHobartAustralia

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