Exploiting Data-Centric Social Context in Phone Call Prediction: A Machine Learning based Study

Sarker, Iqbal (2019) Exploiting Data-Centric Social Context in Phone Call Prediction: A Machine Learning based Study. EAI Endorsed Transactions on Scalable Information Systems, 6 (20): e8. ISSN 2032-9407

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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.

Item Type: Article
Uncontrolled Keywords: Mobile data mining, machine learning, user activity modeling, predictive analytics, personalization, contexts, classification, logistic regression, decision tree, support vector machine, social context, interpersonal relationship, call interruptions, intelligent applications
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 08 Oct 2020 13:57
Last Modified: 08 Oct 2020 13:57
URI: https://eprints.eudl.eu/id/eprint/719

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