User's profile ontology-based semantic framework for personalized food and nutrition recommendation

Ahmed Al-Nazer, Tarek Helmy*, Mohammed Al-Mulhem

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

54 Scopus citations


People depend on popular search engines, like Google and Yahoo, to retrieve the desired information from the Web. Searching for the right food, to eat, is an example of the frequent queries on the Web where people do not find relevant information easily. One reason for this un-satisfaction is the fact that many people have personal preferences where each one likes and dislikes certain food. Also, some people have specific health conditions that restrict their food choices and encourage them to take other food. In addition, the cultures, where people live in, influence food choices and varieties. Therefore, it will be helpful to develop a framework that provides food recommendation, what to take and what to avoid, increasing the advantages and reducing the risks especially for people who have long term diseases such as diabetes and high-blood-pressure. Since health and nutrition information is critical and hence people need to get precise information from trusted sources. Furthermore, transforming the implied knowledge about health and nutrition into structured data is challenging, so developing a framework that semantically manipulate the health and nutrition information is becoming an increasingly important research topic. In this paper, we harness semantic Web and ontology engineering technologies to analyze user's preferences, construct a nutritional and health oriented user's profile, and use the profile to organize the related knowledge so that users can make smarter food and health inquires. We present a semantic framework that uses the personalization techniques based on integrated domain ontologies, pre-constructed by domain experts, to recommend the relevant food that is consistent with people's needs. The empirical evaluation of the proposed framework shows promising results for recommending the relevant food information with a superior user's satisfaction.

Original languageEnglish
Pages (from-to)101-108
Number of pages8
JournalProcedia Computer Science
StatePublished - 2014

Bibliographical note

Funding Information:
The authors would like to acknowledge the support provided by King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at King Fahd University of Petroleum & Minerals for funding this work (project No.10-INF1381-04) as part of the National Science, Technology and Innovation Plan.


  • Food and nutrition
  • Ontology
  • Personalization
  • QWuery manipulation
  • Semantic Web

ASJC Scopus subject areas

  • General Computer Science


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