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.
翻译:推荐系统在当今数字世界中起着重要作用。 开发的推荐系统在音乐平台(例如Spotify)和电影流服务(例如Netflix)等各种应用中找到了应用软件, 例如音乐平台(Spotify)和电影流服务(例如Netflix)等。 对物理练习推荐系统投入了较少的研究工作。 固定的生活方式已成为几种疾病的主要驱动力以及医疗保健费用。 在本文中, 我们根据用户的历史、 概况和类似的用户, 开发了一个日常练习活动的建议系统。 开发的推荐系统使用一个带有用户关注和时间关注机制的深层经常性神经网络。 此外, 练习推荐系统与流动建议系统有很大不同, 我们无法从练习建议系统中的参与者收集反馈。 因此, 我们提议了一个实时的、 即时专家活跃的学习程序。 活跃的学习者计算每个用户每一步骤的建议的不确定性, 并在确定性低时请专家提出建议。 在本文中, 我们得出边缘距离的概率分布功能, 并使用它来确定何时请专家提供反馈。 我们关于移动数据集的实验结果, 显示在实时学习系统之后的准确性学习方法。