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

Harvey, Morgan, Ludwig, Bernd and Elsweiler, David (2013) You Are What You Eat: Learning User Tastes for Rating Prediction. In: String Processing and Information Retrieval. Lecture Notes in Computer Science, 8214 . Springer, London, pp. 153-164. ISBN 978-3-319-02431-8

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Official URL: http://dx.doi.org/10.1007/978-3-319-02432-5_19

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.

Item Type: Book Section
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Related URLs:
Depositing User: Morgan Harvey
Date Deposited: 27 Apr 2015 11:34
Last Modified: 09 May 2017 14:45
URI: http://nrl.northumbria.ac.uk/id/eprint/22220

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