Increasing users' engagement by providing personalized notifications in private social networks for healthcare

在医疗保健的私人社交网络中，患者之间丰富的社交互动加强了 网络提供情感支持和社区感的能力。这种社区感和同伴感 支持对于Curatio提供的私人社交网络的成功至关重要。为了增加这种 社区，我们需要及时主动地通知用户网络中的新事件和交互 这样，当网络中的任何患者发起社交互动时，他/她在网络中的同伴可以 在合理的时间内做出回应。此外，随着网络规模的增长，医疗保健从业人员 在这些网络中扮演支持角色的人将大大受益于智能通知系统， 可以使他们注意到网络中在医学方面具有重要意义的帖子或消息， 需要他们的及时关注 Curatio提供的社交网络产品以移动的平台呈现，这为我们提供了 有机会使用本平台提供的“推送通知”机制，以鼓励用户参与 更多的是在与网络上其他用户的社交互动中。越来越多地用于各种 竞争吸引用户的注意力和时间的移动的应用。然而，很少有研究 这些通知的成功率。我们认为，有必要管理相关性、质量、 频率和发送给用户的通知的时间，以增加通知的有效性和有用性。 通知每个用户。该项目旨在应用机器学习技术来学习用户模型， 通过利用过去的活动数据以及每个用户的 对他/她从系统接收到的通知的反应。

In a private social network for healthcare, abundance of social interactions between patients strengthens the network's ability to provide emotional support and sense of community. This sense of community and peer support is essential for the success of the private social networks provided by Curatio. To increase this sense of community we need to proactively notify users of new events and interactions in the network in a timely manner so that when any patient in the network initiates a social interaction, his/her peers in the network can respond to it in a reasonable time frame. Additionally, as the networks grow in size, the healthcare practitioners who play the support role in these networks would greatly benefit from an intelligent notification system that could bring to their attention the posts or messages in the network that are significant in medical terms and needs their prompt attention. The social network products provided by Curatio are presented in a mobile platform, which gives us the opportunity to use the "push notification" mechanism provided in this platform for encouraging users to engage more often in social interactions with other users on the network. Notifications are increasingly used in various mobile applications which compete for attracting the user's attention and time. However, little research is available on success rate of these notifications. We believe it is necessary to manage the relevance, quality, frequency, and timing of the notifications sent to the user to increase the effectiveness and usefulness of the notifications for each user. This project aims to apply machine learning techniques to learn user models that personalize the above mentioned factors to each user, by utilizing the past activity data as well as each user's reactions to the notifications s/he receives from the system.