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

Morgan Harvey, Bernd Ludwig, David Elsweiler

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

32 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationString Processing and Information Retrieval
Place of PublicationLondon
PublisherSpringer
Pages153-164
Volume8214
ISBN (Print)978-3-319-02431-8
DOIs
Publication statusPublished - 2013
EventString Processing and Information Retrieval - 20th International Symposium, {SPIRE} 2013, Jerusalem, Israel, October 7-9, 2013, Proceedings -
Duration: 1 Jan 2013 → …

Publication series

NameLecture Notes in Computer Science
PublisherSpringer

Conference

ConferenceString Processing and Information Retrieval - 20th International Symposium, {SPIRE} 2013, Jerusalem, Israel, October 7-9, 2013, Proceedings
Period1/01/13 → …

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