@misc{10481/72805,
year = {2022},
month = {1},
url = {http://hdl.handle.net/10481/72805},
abstract = {In recent years, recommender systems have been employed in domains like ecommerce,
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.},
organization = {Consejo Nacional de Ciencia y Tecnologia (CONACyT)},
publisher = {Springer},
keywords = {Recommender systems},
keywords = {Evolutionary computing},
keywords = {Genetic algorithms},
keywords = {Food recommendation},
keywords = {Physical activity recommendation},
keywords = {Well-being},
title = {EvoRecSys: Evolutionary framework for health and well-being recommender systems},
doi = {10.1007/s11257-021-09318-3},
author = {Alcaraz Herrera, Hugo and Palomares, Iván},
}