You Are What You Eat: Learning User Tastes for Rating Prediction

  • Morgan Harvey
  • Bernd Ludwig
  • David Elsweiler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8214)


Poor nutrition is one of the major causes of ill-health and death in the western world and is caused by a variety of factors including lack of nutritional understanding and preponderance towards eating convenience foods. We wish to build systems which can recommend nutritious meal plans to users, however a crucial pre-requisite is to be able to recommend recipes that people will like. In this work we investigate key factors contributing to how recipes are rated by analysing the results of a longitudinal study (n=124) in order to understand how best to approach the recommendation problem. We identify a number of important contextual factors which can influence the choice of rating. Based on this analysis, we construct several recipe recommendation models that are able to leverage understanding of user’s likes and dislikes in terms of ingredients and combinations of ingredients and in terms of nutritional content. Via experiment over our dataset we are able to show that these models can significantly outperform a number of competitive baselines.


Singular Value Decomposition Recommender System Mean Absolute Error Nutritional Information Meal Plan 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Deerwester, S., Dumais, S., Landauer, T., Furnas, G., Harshman, R.: Indexing by lsa. J. of the Am. Soc. of Inf. Sci. 41(6), 391–407 (1990)CrossRefGoogle Scholar
  2. 2.
    Freyne, J., Berkovsky, S.: Intelligent food planning: personalized recipe recommendation. In: 15th Int. Conf. on Intelligent User Interfaces, IUI 2010, pp. 321–324. ACM, New York (2010)Google Scholar
  3. 3.
    Freyne, J., Berkovsky, S., Smith, G.: Recipe recommendation: Accuracy and reasoning. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 99–110. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Hammond, K.: Chef: A model of case-based planning. In: Proceedings of the National Conference on AI (1986)Google Scholar
  5. 5.
    Harvey, M., Carman, M.J., Ruthven, I., Crestani, F.: Bayesian latent variable models for collaborative item rating prediction. In: Proc. CIKM 2011, pp. 699–708. ACM (2011)Google Scholar
  6. 6.
    Harvey, M., Ludwig, B., Elsweiler, D.: Learning user tastes: a first step to generating healthy meal plans? In: ACM RecSys 2012 LifeStyle Workshop (2012)Google Scholar
  7. 7.
    Hinrichs, T.: Strategies for adaptation and recovery in a design problem solver. In: Proceedings of the Workshop on Case-Based Reasoning (1989)Google Scholar
  8. 8.
    Mueller, M., Harvey, M., Elsweiler, D., Mika, S.: Ingredient matching to determine the nutr. properties of internet-sourced recipes. In: Pervasive Health (2012)Google Scholar
  9. 9.
    Nestle, M., Wing, R., Birch, L., DiSogra, L., Drewnowski, A., Middleton, S., Sigman-Grant, M., Sobal, J., Winston, M., Economos, C.: Behavioral and social influences on food choice. Nutrition Reviews 56(5), 50–64 (1998)CrossRefGoogle Scholar
  10. 10.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Rec. Systems Handbook. Springer (2011)Google Scholar
  11. 11.
    Salton, G., Buckley, C.: Weighting approaches in automatic text retrieval. IP and M 24(5), 513–523 (1988)Google Scholar
  12. 12.
    Scheibehenne, B., Greifeneder, R., Todd, P.M.: Can there ever be too many options? A meta-analytic review of choice overload. J. of Consumer Rsrch. 37, 409–425 (2010)CrossRefGoogle Scholar
  13. 13.
    Sobecki, J., Babiak, E., Słanina, M.: Application of hybrid recommendation in web-based cooking assistant. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4253, pp. 797–804. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Svensson, M., Laaksolahti, J., Höök, K., Waern, A.: A recipe based on-line food store. In: 5th Int. Conf. on Intelligent User Interfaces, IUI 2000, pp. 260–263. ACM, New York (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Morgan Harvey
    • 1
  • Bernd Ludwig
    • 2
  • David Elsweiler
    • 2
  1. 1.Faculty of InformaticsUniversity of LuganoLuganoSwitzerland
  2. 2.Inst. for Info. and Media, Lang. and CultureUniversity of RegensburgGermany

Personalised recommendations