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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)

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.

Keywords

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.

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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

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