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Recommender Systems have not been explored to a great extent for improvinghealth and subjective wellbeing. Recent advances in mobile technologies anduser modelling present the opportunity for delivering such systems, however thekey issue is understanding the drivers of subjective wellbeing at an individuallevel. In this paper we propose a novel approach for deriving personalizedactivity recommendations to improve subjective wellbeing by maximizing thecongruence between activities and personality traits. To evaluate the model, weleveraged a rich dataset collected in a smartphone study, which contains threeweeks of daily activity probes, the Big-Five personality questionnaire andsubjective wellbeing surveys. We show that the model correctly infers a rangeof activities 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.

Aligning Daily Activities with Personality: Towards A Recommender System for Improving Wellbeing