  Recommendation systems play an important role in today's digital world. They
have found applications in various applications such as music platforms, e.g.,
Spotify, and movie streaming services, e.g., Netflix. Less research effort has
been devoted to physical exercise recommendation systems. Sedentary lifestyles
have become the major driver of several diseases as well as healthcare costs.
In this paper, we develop a recommendation system for daily exercise activities
to users based on their history, profile and similar users. The developed
recommendation system uses a deep recurrent neural network with user-profile
attention and temporal attention mechanisms.
  Moreover, exercise recommendation systems are significantly different from
streaming recommendation systems in that we are not able to collect click
feedback from the participants in exercise recommendation systems. Thus, we
propose a real-time, expert-in-the-loop active learning procedure. The active
learners calculate the uncertainty of the recommender at each time step for
each user and ask an expert for a recommendation when the certainty is low. In
this paper, we derive the probability distribution function of marginal
distance, and use it to determine when to ask experts for feedback. Our
experimental results on a mHealth dataset show improved accuracy after
incorporating the real-time active learner with the recommendation system.
