Exploiting Data-Centric Social Context in Phone Call Prediction: A Machine Learning based Study - Sarker, Iqbal H. (EAI Endorsed Transactions on Scalable Information Systems, 2019)
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.