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

Abstract

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.

A Comprehensive User Modeling Framework and a Recommender System for Personalizing Well-Being Related Behavior Change Interventions: Development and Evaluation

“…In real-life studies, these metrics are inconvenient because requiring users to rate all the available items for identifying the true negatives and positives would significantly increase user burden and hamper the real-life setting. Instead, user satisfaction with the recommended items (e.g., [33], [38], [44]), self-reported or observed compliance to recommendations (e.g., [24], [30], [35]), and changes in health outcomes (e.g., [24], [38], [46]) have been reported for assessing the suitability of recommendations. In addition, user experience, perceived usefulness, and usability of HRSs are typically assessed, but with varied self-report scales or interview questions [18].…”

“…In addition, many HRSs utilize context-related features to determine opportune moments for delivering recommendations, of which location and time of day are the most prevalent (e.g., [24], [26]- [28], [33]), but calendar availability [27], [28] and users' momentary activities [24], [26] have also been used in a few examples. Considering user preferences (e.g., preferred PA modalities and times, dietary restrictions) [20], [25]- [27], [44], [45] and the usefulness or effectiveness of recommendations (either user-evaluated or inferred) [24], [28], [30], [35], [38], [43] are also quite common. Some HRSs consider mental state (e.g., stress level, mood) [28], [29], [47], social ties [27], [33], environmental conditions [22], [47], or personal traits [28]…”

“…In current HRS research, the attempts to improve the performance of a HRS are mostly focused on finding the most accurate recommendation techniques (e.g., [27], [30], [34], [44]), while user models have attracted less research interest, even though wisely chosen user features can increase the suitability of recommendations significantly, which is important for improved user engagement and a positive health impact. The VI model provides a common user model framework that serves different personalization goals by considering not only the health and behavior change needs of individuals, which are the most widely used features for personalization and, beyond doubt, the most important ones in terms of the expected health impact, but also various other factors that influence user engagement and adherence to intervention.…”

“…The time lag between receiving and reading messages has been used to infer the best time to disrupt a user [39]. In addition, user preferences regarding physical activity (PA) modes and food items have been used to personalize recommendations [25]- [27], [44], [45]. However, we could not find examples that attempted to make behavior change objectives personally meaningful or personalized the tone of messages.…”

CarpeDiem: Exploring health pillars to define a holistic recommender system to accomplish a sutainable change in lifestlye habits

“…Currently, the vast majority of recommender systems focus on a single aspect of health [22]. A variety of devices and technological solutions can be used to achieve a healthier lifestyle.…”