Abstract
Context-awareness in phone call prediction can help us to build many intelligent applications to assist the end mobile phone users in their daily life. Social context, particularly, the interpersonal relationship between individuals, is one of the key contexts for modeling and predicting mobile user phone call activities. Individual’s diverse call activities, such as making a phone call to a particular person, or responding an incoming call are not identical to all; may differ from person-to-person based on their interpersonal relationships, such as family, friend, or colleague. However, it is very difficult to make the device understandable about such semantic relationships in phone call prediction. Thus, in this paper, we explore the data-centric social relational context generating from the mobile phone data, which can play a significant role to achieve our goal. To show the effectiveness of such contextual information in prediction model, we conduct our study using the most popular machine learning classification techniques, such as logistic regression, decision tree, and support vector machine, utilizing individual’s mobile phone data.
Highlights
Nowadays, mobile phones have become an essential part of our daily life
We have conducted experiments on five phone log datasets, collected by Massachusetts Institute of Technology (MIT) for their Reality Mining project [6]. These are represented as D1, D2,...,D5 in our experiments. These datasets are collected over a period of 9 months, which include individual’s diverse phone call activities, such as accepting an incoming call, rejecting an incoming call, missed call, and making an outgoing call, and corresponding contextual information that are used in our machine learning based model
We show the impact of data-centric social context in the prediction model
Summary
Mobile phones have become an essential part of our daily life. The number of mobile cellular subscriptions is almost equal to the number of people on the planet [9]. An “intelligent phone call interruption management system” could be a real-life application based on the relevant social contexts, which handles the incoming phone calls automatically according to the activities of an individual user. We explore data-centric social context, i.e., how individuals’ phone numbers available in the dataset, represents interpersonal relationships between them in order to model and predict their phone call activities based on machine learning techniques. The importance of this data-centric social context in mobile applications, has been highlighted in our earlier paper [13]. We discuss two real-world contactspecific applications based on social relational context in Section 6, and Section 7 concludes this paper
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