تقرير
Aligning Daily Activities with Personality: Towards A Recommender System for Improving Wellbeing
| العنوان: | Aligning Daily Activities with Personality: Towards A Recommender System for Improving Wellbeing |
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| المؤلفون: | Khwaja, Mohammed, Ferrer, Miquel, Iglesias, Jesus Omana, Faisal, A. Aldo, Matic, Aleksandar |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Human-Computer Interaction |
| الوصف: | 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. Comment: Presented at ACM Conference on Recommender Systems (RecSys) 2019, Copenhagen, Denmark |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1909.03847 |
| رقم الأكسشن: | edsarx.1909.03847 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |