Advertisement

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)

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.

Keywords

Collaborative filtering content-based machine learning recipes personalization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chronic disease information sheet, http://www.who.int/mediacentre/factsheets/fs311/en/index.html (accessed June 2010)
  2. 2.
    Freyne, J., Berkovsky, S.: Intelligent Food Planning: Personalized Recipe Recommendation. In: Proceedings of the 2010 International Conference on Intelligent User Interfaces (IUI 2010), pp. 321–324 (2010)Google Scholar
  3. 3.
    Freyne, J., Berkovsky, S.: Recommending Food: Reasoning on Recipes and Ingredients. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 381–386. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Hall, M.: Correlation-based feature selection for machine learning. PhD thesis, Citeseer (1999)Google Scholar
  5. 5.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The Weka Data Mining Software: An Update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)CrossRefGoogle Scholar
  6. 6.
    Hammond, K.: CHEF: A Model of Case-Based Planning. In: Proceedings of the Fifth National Conference on Artificial Intelligence, vol. 1 (1986)Google Scholar
  7. 7.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR, pp. 230–237 (1999)Google Scholar
  8. 8.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  9. 9.
    Hinrichs, T.R.: Strategies for adaptation and recovery in a design problem solver. In: Proceedings of the Workshop on Case-Based Reasoning (1989)Google Scholar
  10. 10.
    Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM 40(3), 87 (1997)CrossRefGoogle Scholar
  11. 11.
    Noakes, M., Clifton, P.: The CSIRO Total Wellbeing Diet Book. Penguin Group, Australia (2005)Google Scholar
  12. 12.
    Noakes, M., Clifton, P.: The CSIRO Total Wellbeing Diet Book 2. Penguin Group, Australia (2006)Google Scholar
  13. 13.
    Quinlan, J.: Learning with continuous classes. In: Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, pp. 343–348. Citeseer (1992)Google Scholar
  14. 14.
    Svensson, M., Höök, K., Laaksolahti, J., Waern, A.: Social navigation of food recipes. In: CHI 2001: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 341–348. ACM, New York (2001)Google Scholar
  15. 15.
    van Pinxteren, Y., Geleijnse, G., Kamsteeg, P.: Deriving a recipe similarity measure for recommending healthful meals. In: Proceedings of the 2011 International Conference on Intelligent User Interfaces, IUI 2011, pp. 105–114 (2011)Google Scholar
  16. 16.
    Wang, Y., Witten, I.: Induction of model trees for predicting continuous classes (1996)Google Scholar
  17. 17.
    Zhang, Q., Hu, R., Namee, B., Delany, S.: Back to the future: Knowledge light case base cookery. Technical report, Dublin Institute of Technology (2008)Google Scholar

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

Personalised recommendations