Aligning Daily Activities with Personality: Towards A Recommender System for Improving Wellbeing
This addresses the challenge of personalizing health and wellbeing recommendations for individuals, though it appears incremental as it applies existing recommender system concepts to a new domain.
The paper tackles the problem of improving subjective wellbeing by proposing a recommender system that aligns daily activities with individual personality traits, showing that the model correctly infers activities positively or negatively related to wellbeing and matches real-world outcomes.
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.