EvoRecSys: Evolutionary Framework for Health and Wellbeing Recommender Systems

Hugo Alcaraz-Herrera, John Cartlidge, Zoi Toumpakari, Max Western, Iván Palomares

Research output: Contribution to journalArticlepeer-review

13 Citations (SciVal)


In recent years, recommender systems have been employed in domains like e-commerce, tourism, and multimedia streaming, where personalising users’ experience based on their interactions is a fundamental aspect to consider. Recent recommender system developments have also focused on well-being, yet existing solutions have been entirely designed considering one single well-being aspect in isolation, such as a healthy diet or an active lifestyle. This research introduces EvoRecSys, a novel recommendation framework that proposes evolutionary algorithms as the main recommendation engine, thereby modelling the problem of generating personalised well-being recommendations as a multi-objective optimisation problem. EvoRecSys captures the interrelation between multiple aspects of well-being by constructing configurable recommendations in the form of bundled items with dynamic properties. The preferences and a predefined well-being goal by the user are jointly considered. By instantiating the framework into an implemented model, we illustrate the use of a genetic algorithm as the recommendation engine. Finally, this implementation has been deployed as a Web application in order to conduct a users’ study.

Original languageEnglish
Pages (from-to)883-921
Number of pages39
JournalUser Modeling and User-Adapted Interaction
Issue number5
Early online date31 Jan 2022
Publication statusPublished - 30 Nov 2022

Bibliographical note

Funding Information:
Hugo Alcaraz-Herrera’s PhD is supported by The Mexican Council of Science and Technology (Consejo Nacional de Ciencia y Tecnología - CONACyT).


  • Evolutionary computing
  • Food recommendation
  • Genetic algorithms
  • Physical activity recommendation
  • Recommender systems
  • Well-being

ASJC Scopus subject areas

  • Education
  • Human-Computer Interaction
  • Computer Science Applications


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