Recipe recommendation: accuracy and reasoning

Jill Freyne, Shlomo Berkovsky, Gregory Smith

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

25 Citations (Scopus)


Food and diet are complex domains for recommender technology, but the need for systems that assist users in embarking on and engaging with healthy living programs has never been more real. One key to sustaining long term engagement with eHealth services is the provision of tools, which assist and train users in planning correctly around the areas of diet and exercise. These tools require an understanding of user reasoning as well as user needs and are ideal application areas for recommender and personalization technologies. Here, we report on a large scale analysis of real user ratings on a set of recipes in order to judge the applicability and practicality of a number of personalization algorithms. Further to this, we report on apparent user reasoning patterns uncovered in rating data supplied for recipes and suggest ways to exploit this reasoning understanding in the recommendation process.
Original languageEnglish
Title of host publicationUser modeling, adaptation, and personalization
Subtitle of host publication19th International Conference, UMAP 2011 Proceedings
EditorsJoseph A. Konstan, Ricardo Conejo, José L. Marzo, Nuria Oliver
Place of PublicationHeidelberg
PublisherSpringer, Springer Nature
Number of pages12
ISBN (Electronic)9783642223624
ISBN (Print)9783642223617
Publication statusPublished - 2011
Externally publishedYes
EventUser Modeling, Adaptation and Personalization Conference, UMAP 2011 - Girona, Spain
Duration: 11 Jul 201115 Jul 2011

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherUser Modeling, Adaptation and Personalization Conference, UMAP 2011


  • collaborative filtering
  • content-based
  • machine learning
  • recipes
  • personalization

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