Context-aware Adaptive Recommendation System for Personal Well-being Services

Nowadays, a healthy lifestyle is an essential requirement in people's daily life. Although well-being recommendation systems have been extensively explored in different domains, there are still some challenges for developing efficient recommendation systems dealing with the limitations of content-based recommendation approaches. In this paper, a context-aware adaptive recommendation system is proposed to provide personal wellbeing services intended to help people to have a healthy lifestyle in Ambient Assisted Living (AAL) systems. The recommendations are based on people's behaviors. Machine-learning models are firstly used to recognize human activities, locations, and objects. The different contexts of human behaviors, including location, object, frequency, duration, and sequences of frequent activities, are then extracted. An ontology, called Human ActiVity ONtology (HAVON) ontology, is used to conceptualize human activities and their contexts. Finally, a probabilistic version of Answer set Programming (ASP), a high-level expressive logic-based formalism, is proposed to provide adaptive recommendations through a set of probabilistic rules based on human behaviors. A companion robot, called Pepper, is used for the evaluation of the proposed recommendation system. The evaluation results demonstrate the ability of the proposed system to help people to have a healthy lifestyle.