Abstract
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.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02432-5_33
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
Hammond, K.: Chef: A model of case-based planning. In: Proceedings of the National Conference on AI (1986)
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)
Harvey, M., Ludwig, B., Elsweiler, D.: Learning user tastes: a first step to generating healthy meal plans? In: ACM RecSys 2012 LifeStyle Workshop (2012)
Hinrichs, T.: Strategies for adaptation and recovery in a design problem solver. In: Proceedings of the Workshop on Case-Based Reasoning (1989)
Mueller, M., Harvey, M., Elsweiler, D., Mika, S.: Ingredient matching to determine the nutr. properties of internet-sourced recipes. In: Pervasive Health (2012)
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)
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Rec. Systems Handbook. Springer (2011)
Salton, G., Buckley, C.: Weighting approaches in automatic text retrieval. IP and M 24(5), 513–523 (1988)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Harvey, M., Ludwig, B., Elsweiler, D. (2013). You Are What You Eat: Learning User Tastes for Rating Prediction. In: Kurland, O., Lewenstein, M., Porat, E. (eds) String Processing and Information Retrieval. SPIRE 2013. Lecture Notes in Computer Science, vol 8214. Springer, Cham. https://doi.org/10.1007/978-3-319-02432-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-02432-5_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02431-8
Online ISBN: 978-3-319-02432-5
eBook Packages: Computer ScienceComputer Science (R0)