Recommender Systems have not been explored to a great extentfor improving health and subjective wellbeing. Recent advances inmobile technologies and user modelling present the opportunityfor delivering such systems, however the key issue is understand-ing the drivers of subjective wellbeing at an individual level. Inthis paper we propose a novel approach for deriving personalizedactivity recommendations to improve subjective wellbeing by maxi-mizing the congruence between activities and personality traits. Toevaluate the model, we leveraged a rich dataset collected in a smart-phone study, which contains three weeks of daily activity probes,the Big-Five personality questionnaire and subjective wellbeingsurveys. We show that the model correctly infers a range of activ-ities that are ’good’ or ’bad’ (i.e. that are positively or negativelyrelated to subjective wellbeing) for a given user and that the derivedrecommendations greatly match outcomes in the real-world.