EvoRecSys: Evolutionary framework for health and well-being recommender systems

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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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
JournalUser Modeling and User-Adapted Interaction
Publication statusAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Education
  • Human-Computer Interaction
  • Computer Science Applications


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