SilentPhone: Inferring User Unavailability based Opportune Moments to Minimize Call Interruptions
This addresses the issue of inappropriate call interruptions for mobile phone users and their surroundings, but it is incremental as it builds on existing data-driven methods for personalization.
The paper tackles the problem of minimizing call interruptions by inferring opportune moments for phone calls based on user unavailability, using a data-driven approach that analyzes past phone log data to generate silent mode configuring rules, with experiments on real mobile phone datasets showing it can identify these moments and capture user behavior patterns.
The increasing popularity of cell phones has made them the most personal and ubiquitous communication devices nowadays. Typically, the ringing notifications of mobile phones are used to inform the users about the incoming calls. However, the notifications of inappropriate incoming calls sometimes cause interruptions not only for the users but also the surrounding people. In this paper, we present a data-driven approach to infer the opportune moments for such phone call interruptions based on user's unavailability, i.e., when a user is unable to answer the incoming phone calls, by analyzing individual's past phone log data, and to discover the corresponding phone silent mode configuring rules for the purpose of minimizing call interruptions in an automated intelligent system. Experiments on the real mobile phone datasets show that our approach is able to identify the opportune moments for call interruptions and generates corresponding silent mode configuring rules by capturing the dominant behavior of individual users' at various times-of-the-day and days-of-the week.