  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.
