Evaluation of an Engagement-Aware Recommender System for People with Dementia

Abstract

People with Dementia (PwD) and their caregivers can greatly benefit from regular cognitive and social activations. However, these activations need to be engaging and likeable to take effect and to maintain long-term motivation and wellbeing. Taking this into account, finding appropriate items in large activation content catalogues can be a challenging task, which can even lead to unhappiness ("Paradox of Choice"). User-centered Recommender Systems (RS) can help to overcome this obstacle and support PwD and their caregivers in finding engaging and likeable activation contents. In this study, we investigate a dataset collected from PwD and their (in)formal caregivers who jointly used a tablet-based activation system over multiple sessions in an unconstrained care setting. The system applies a content-based recommendation approach based on explicit ratings provided by the PwD and collects audiovisual data during usage. First, we evaluate the real-world user interactions with the RS to gain knowledge about suitable evaluation parameters for our offline analyses. Second, we train a recognition model for engagement based on the audiovisual data and enrich our dataset with the automatically detected information about the PwD's level of engagement. Last, we apply an offline analysis and compare the RS performance based on different inputs. We show that considering PwD's level of engagement can help to further improve the rating-based RS in terms of users' needs and, thus, support them in the activations.