Notifications provide a unique mechanism for increasing the effectiveness of real-time information delivery systems. However, notifications that demand users' attention at inopportune moments are more likely to have adverse effects and might become a cause of potential disruption rather than proving beneficial to users. In order to address these challenges a variety of intelligent notification mechanisms based on monitoring and learning users' behavior have been proposed. The goal of such mechanisms is maximizing users' receptivity to the delivered information by automatically inferring the right time and the right context for sending a certain type of information. This article presents an overview of the current state of the art in the area of intelligent notification mechanisms that relies on the awareness of users' context and preferences. More specifically, we first present a survey of studies focusing on understanding and modeling users' interruptibility and receptivity to notifications from desktops and mobile devices. Then, we discuss the existing challenges and opportunities in developing mechanisms for intelligent notification systems in a variety of application scenarios.  Mobile phones represent an essential element of our lives by assisting us with multifarious dayto-day activities. At the same time, they are always connected to the Internet. For these reasons, they represent a unique platform for receiving or fetching information anytime and anywhere. For example, they can be used to keep users aware of information channels through mobile apps such as Skype, Hangouts, Facebook, Twitter and Gmail. In order to ensure real-time awareness of users about the delivered information, mobile operating systems rely on notifications that steer users' attention towards the delivered information through audio, visual and haptic signals. This is indeed in contrast with the traditional paradigm of pull-based information retrieval and delivery in which the user has to initiate a request for the transmission of information. Notifications are the cornerstone of push-based information delivery via mobile phones as they allow applications to harness the opportunity of steering users' attention towards Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. the delivered information in order to maximize its effectiveness. Mobile notifications are presented in a unified fashion by almost all mobile operating systems. Usually, in the current implementations, notifications from all applications are listed in a notification bar at the top of a phone's screen. In order to provide a quick glimpse of the delivered information to the users, they present a brief summary including the identity of the sender, a brief description or summary of the content of the notifications or the event that trigger them, and time of delivery. Mobile notifications are generated by humans as well as machines. The former are generated by recipient's social connections generally through chat and email applications for instantiating communication between two or more persons, whereas, in the latter case, messages are generated in an automatic fashion by the system processes or the native applications, such as system monitoring utilities, scheduled reminders and promotional advertisements. Nowadays, since a variety of sophisticated sensors are embedded in phones, the data generated therefrom is exploited by sophisticated applications for not just improving usability but also for proactively signaling users about the occurrence of events that are associated with their context. Some common example of context-based notifications are collocation-based advertisements [1, 32] and context-based suggestions [57, 97] . Push notifications (as shown in Figure 1 ) were introduced in mobile phones to keep users free from constantly checking for (i.e., pulling) new information, as they signal users on the availability of any newly arrived information. However, people receive numerous notifications arriving autonomously at anytime during the day through their mobile apps [75, 94] . Such services provide a clear benefit to users as they facilitate task switching and keep users aware of a number of information channels in an effortless manner. However, at the same time, these notifications are often triggered at inappropriate times as they do not have any knowledge about recipients' situation. Psychological studies have found that notifications arriving at unsuitable moments often become a cause of disruption for the on-going task [80, 105] . Notifications delivered at the wrong time can adversely affect the current primary task's execution [20, 21, 81? ] and users' affective state [2, 7] . On the other hand, a previous study has found that in order to not miss any newly available information that is deemed important, people are willing to tolerate mobile interruptions [50] . However, various mobile applications exploit users' willingness by triggering a large number of
At the same time, the advances in the area of machine learning have enabled us to model users' interruptibility by exploiting their historical notication interaction data REF .
Intelligent Notification Systems: A Survey of the State of the Art and Research Challenges
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