Wellbeing Recommender System, a User-Centered Framework for Generating a Recommender System for Healthy Aging
Authors: Meritxell García Perea Garazi Artola Balda Isabel Amaya Rodriguez Nekane Larburu Rubio
Date: 01.04.2023
Abstract
The needs of the currently aging population require new technologies to support them in order to offer them a decent quality of life. Different interventions have been proposed in the last years to face this challenge, where recommender systems are gaining strength. The general objective of these systems is to promote the adoption of healthy habits among the end users, but sometimes they show limitations in the fulfilment of this goal. To overcome these limitations, our approach offers an easy to maintain, interoperable, and personalized recommender system capable of providing recommendations based on individuals' daily activity data. A methodology is presented for the generation and management of wellbeing recommendations, which are then tested using a synthetically generated dataset that simulates a variety of user categories. With the evaluation of this data, a technical validation is carried on to assess the performance and scalability of our developed system.
BIB_text
title = {Wellbeing Recommender System, a User-Centered Framework for Generating a Recommender System for Healthy Aging},
pages = {118-125},
keywds = {
Healthy Aging; Quality of Life; Recommender System; Synthetic Data Generation
}
abstract = {
The needs of the currently aging population require new technologies to support them in order to offer them a decent quality of life. Different interventions have been proposed in the last years to face this challenge, where recommender systems are gaining strength. The general objective of these systems is to promote the adoption of healthy habits among the end users, but sometimes they show limitations in the fulfilment of this goal. To overcome these limitations, our approach offers an easy to maintain, interoperable, and personalized recommender system capable of providing recommendations based on individuals' daily activity data. A methodology is presented for the generation and management of wellbeing recommendations, which are then tested using a synthetically generated dataset that simulates a variety of user categories. With the evaluation of this data, a technical validation is carried on to assess the performance and scalability of our developed system.
}
isbn = {978-989758645-3},
doi = {10.5220/0011760600003476},
date = {2023-04-01},
}
