Aligning daily activities with personality

Abstract

Recommender Systems have not been explored to a great extent for improving health and subjective wellbeing. Recent advances in mobile technologies and user modelling present the opportunity for delivering such systems, however the key issue is understanding the drivers of subjective wellbeing at an individual level. In this paper we propose a novel approach for deriving personalized activity recommendations to improve subjective wellbeing by maximizing the congruence between activities and personality traits. To evaluate the model, we leveraged a rich dataset collected in a smartphone study, which contains three weeks of daily activity probes, the Big-Five personality questionnaire and subjective wellbeing surveys. We show that the model correctly infers a range of activities that are 'good' or 'bad' (i.e. that are positively or negatively related to subjective wellbeing) for a given user and that the derived recommendations greatly match outcomes in the real-world.

Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study

“…Although the potential of recommended systems has been occasionally explored in health care research [61][62][63], the possibility of taking advantage of this technology to improve mental health care is yet to be sufficiently explored. Mindcraft presents the opportunity for an enhanced recommendation system to be developed where recommendations may be delivered based on personality, along with other factors such as demographics, behavior, and self-reported scores for specific symptoms.…”

Sports Recommender Systems: Overview and Research Issues

“…Such recommendations can be based on collaborative filtering which better support serendipity effects, content-based recommendation with a focus on identifying very similar routes, and knowledge-based (specifically critiquing-based [25]) recommendation if the goal is to explore the set of possible routes. In this context, items (e.g., routes or training sessions) should be recommended in such a way that user engagement is maintained in the long run [13,122] and specific physical conditions (e.g., recommendation of physical practices for underweights [68]) and psychological aspects (e.g., personality-aware recommendations [71]) of persons are taken into account.…”