Recommending food: Reasoning on recipes and ingredients

Jill Freyne, Shlomo Berkovsky

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

55 Citations (Scopus)


With the number of people considered to be obese rising across the globe, the role of IT solutions in health management has been receiving increased attention by medical professionals in recent years. This paper focuses on an initial step toward understanding the applicability of recommender techniques in the food and diet domain. By understanding the food preferences and assisting users to plan a healthy and appealing meal, we aim to reduce the effort required of users to change their diet. As an initial feasibility study, we evaluate the performance of collaborative filtering, content-based and hybrid recommender algorithms on a dataset of 43,000 ratings from 512 users. We report on the accuracy and coverage of the algorithms and show that a content-based approach with a simple mechanism that breaks down recipe ratings into ingredient ratings performs best overall.
Original languageEnglish
Title of host publicationUser Modeling, Adaptation, and Personalization
Subtitle of host publication18th International Conference, UMAP 2010 Proceedings
EditorsPaul De Bra, Alfred Kobsa, David Chin
Place of PublicationBerlin
PublisherSpringer, Springer Nature
Number of pages6
ISBN (Electronic)9783642134708
ISBN (Print)9783642134692
Publication statusPublished - 2010
Externally publishedYes
Event18th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2010 - Big Island, United States
Duration: 20 Jun 201024 Jun 2010

Publication series

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


Conference18th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2010
Country/TerritoryUnited States
CityBig Island


  • collaborative filtering
  • content-based
  • ingredient
  • recipes


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