Predicting mobile call behavior via subspace methods

Peng Dai, Wanqing Yang, Shen Shyang Ho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We investigate behavioral prediction approaches based on subspace methods such as principal component analysis (PCA) and independent component analysis (ICA). Moreover, we propose a personalized sequential prediction approach to predict next day behavior based on features extracted from past behavioral data using subspace methods. The proposed approach is applied to the individual call (voice calls and short messages) behavior prediction task. Experimental results on the Nokia mobility data challenge (MDC) dataset are used to show the feasibility of our proposed prediction approach. Furthermore, we investigate whether prediction accuracy can be improved (i) when specific call type (voice call or short message), instead of the general call behavior prediction, is considered in the prediction task, and (ii) when workday and weekend scenarios are considered separately.

Original languageEnglish (US)
Title of host publicationSocial Computing, Behavioral-Cultural Modeling and Prediction - 6th International Conference, SBP 2013, Proceedings
Pages466-475
Number of pages10
DOIs
StatePublished - Mar 14 2013
Externally publishedYes
Event6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013 - Washington, DC, United States
Duration: Apr 2 2013Apr 5 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7812 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013
Country/TerritoryUnited States
CityWashington, DC
Period4/2/134/5/13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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