Exploiting contextual information to improve call prediction

Autoři: Mehk Fatima aff001;  Aimal Rextin aff002;  Shamaila Hayat aff002;  Mehwish Nasim aff003
Působiště autorů: Department of Computer Science & Information Technology, University of Lahore, Gujrat Campus, Gujrat, Pakistan aff001;  Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan aff002;  Data61, CSIRO, Adelaide, Australia aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0223780


With the increase in contact list size of mobile phone users, the management and retrieval of contacts has becomes a tedious job. In this study, we analysed some important dimensions that can effectively contribute in predicting which contact a user is going to call at time t. We improved a state of the art algorithm, that uses frequency and recency by adding temporal information as an additional dimension for predicting future calls. The proposed algorithm performs better in overall analysis, but more significantly there was an improvement in the prediction of top contacts of a user as compared to the base algorithm.

Klíčová slova:

Algorithms – Behavior – Cell phones – Circadian rhythms – Quantitative analysis – Social communication – Switzerland – Information retrieval


1. The world in 2014 ICT Facts and Figures Data. Statistics Division, Telecommunication Development Bureau,International Telecommunication Union,ICT.; 2015.

2. Bentley FR, Chen YY. The Composition and Use of Modern Mobile Phonebooks. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. CHI’15. New York, NY, USA: ACM; 2015. p. 2749–2758. Available from: http://doi.acm.org/10.1145/2702123.2702182.

3. Nasim M, Rextin A, Hayat S, Khan N, Malik MM. Data analysis and call prediction on dyadic data from an understudied population. Pervasive and Mobile Computing. 2017;41:166–178. https://doi.org/10.1016/j.pmcj.2017.08.002.

4. Komninos A, Liarokapis D. The use of mobile contact list applications and a context-oriented framework to support their design. In: Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM; 2009. p. 79.

5. Bergman O, Komninos A, Liarokapis D, Clarke J. You never call: Demoting unused contacts on mobile phones using DMTR. Personal and Ubiquitous Computing. 2012;16(6):757–766. doi: 10.1007/s00779-011-0411-3

6. Plessas A, Stefanis V, Komninos A, Garofalakis J. Field evaluation of context aware adaptive interfaces for efficient mobile contact retrieval. Pervasive and Mobile Computing. 2017;35:51–64. https://doi.org/10.1016/j.pmcj.2016.04.011.

7. Salehan M, Negahban A. Social networking on smartphones: When mobile phones become addictive. Computers in Human Behavior. 2013;29(6):2632–2639. https://doi.org/10.1016/j.chb.2013.07.003.

8. Stefanis V, Plessas A, Komninos A, Garofalakis J. Frequency and recency context for the management and retrieval of personal information on mobile devices. Pervasive and Mobile Computing. 2014;.

9. Nasim M, Rextin A, Khan N, Malik MM. Understanding call logs of smartphone users for making future calls. In: MobileHCI; 2016.

10. Kiukkonen N, J B, Dousse O, Gatica-Perez D, Laurila JK. Towards rich mobile phone datasets: Lausanne data collection campaign. In: Proc. ACM Int. Conf. on Pervasive Services (ICPS,’,’,’), Berlin.; 2010.

11. Miller G. The Smartphone Psychology Manifesto. Perspectives on Psychological Science. 2012;7(3):221–237. doi: 10.1177/1745691612441215 26168460

12. de Montjoye YA, Quoidbach J, Robic F, Pentland AS. Predicting Personality Using Novel Mobile Phone-Based Metrics. In: Greenberg AM, Kennedy WG, Bos ND, editors. Social Computing, Behavioral-Cultural Modeling and Prediction. Berlin, Heidelberg: Springer Berlin Heidelberg; 2013. p. 48–55.

13. Phithakkitnukoon S, Horanont T, Di Lorenzo G, Shibasaki R, Ratti C. Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data. In: Salah AA, Gevers T, Sebe N, Vinciarelli A, editors. Human Behavior Understanding. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010. p. 14–25.

14. Azevedo TS, Bezerra RL, Campos CAV, de Moraes LFM. An Analysis of Human Mobility Using Real Traces. In: 2009 IEEE Wireless Communications and Networking Conference; 2009. p. 1–6.

15. Hoteit S, Secci S, Sobolevsky S, Ratti C, Pujolle G. Estimating Human Trajectories and Hotspots Through Mobile Phone Data. Comput Netw. 2014;64:296–307. doi: 10.1016/j.comnet.2014.02.011

16. LiKamWa R, Liu Y, Lane N, Zhong L. MoodScope: Building a mood sensor from smartphone usage patterns. In: MobiSys 2013 - Proceedings of the 11th Annual International Conference on Mobile Systems, Applications, and Services; 2013. p. 389–401.

17. de Oliveira R, Karatzoglou A, Cerezo PC, de Vicuña AAL, Oliver N. Towards a psychographic user model from mobile phone usage. In: CHI Extended Abstracts; 2011.

18. Chittaranjan G, Blom J, Gatica-Perez D. Mining Large-scale Smartphone Data for Personality Studies. Personal Ubiquitous Comput. 2013;17(3):433–450. doi: 10.1007/s00779-011-0490-1

19. Vieira MR, Frias-Martinez V, Oliver N, Frias-Martinez E. Characterizing Dense Urban Areas from Mobile Phone-Call Data: Discovery and Social Dynamics. In: 2010 IEEE Second International Conference on Social Computing; 2010. p. 241–248.

20. Wang H, Calabrese F, Di Lorenzo G, Ratti C. Transportation mode inference from anonymized and aggregated mobile phone call detail records. In: 13th International IEEE Conference on Intelligent Transportation Systems; 2010. p. 318–323.

21. Berlingerio M, Calabrese F, Di Lorenzo G, Nair R, Pinelli F, Sbodio ML. AllAboard: A System for Exploring Urban Mobility and Optimizing Public Transport Using Cellphone Data. In: Blockeel H, Kersting K, Nijssen S, Železný F, editors. Machine Learning and Knowledge Discovery in Databases. Berlin, Heidelberg: Springer Berlin Heidelberg; 2013. p. 663–666.

22. Eagle N, Pentland AS, Lazer D. Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences. 2009;106(36):15274–15278. doi: 10.1073/pnas.0900282106

23. Dong Y, Yang Y, Tang J, Yang Y, Chawla NV. Inferring User Demographics and Social Strategies in Mobile Social Networks. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD’14. New York, NY, USA: ACM; 2014. p. 15–24. Available from: http://doi.acm.org/10.1145/2623330.2623703.

24. Phithakkitnukoon S, Dantu R. Predicting Calls—New Service for an Intelligent Phone. In: Krishnaswamy D, Pfeifer T, Raz D, editors. Real-Time Mobile Multimedia Services. Berlin, Heidelberg: Springer Berlin Heidelberg; 2007. p. 26–37.

25. Phithakkitnukoon S, Dantu R, Claxton R, Eagle N. Behavior-based Adaptive Call Predictor. ACM Trans Auton Adapt Syst. 2011;6(3):21:1–21:28. doi: 10.1145/2019583.2019588

26. Haddad MR, Baazaoui H, Ziou D, Ghezala HB. A predictive model for recurrent consumption behavior: An application on phone calls. Knowledge-Based Systems. 2014;64:32–43. https://doi.org/10.1016/j.knosys.2014.03.018.

27. Barzaiq OO, Loke SW. Adapting the mobile phone for task efficiency: the case of predicting outgoing calls using frequency and regularity of historical calls. Personal and Ubiquitous Computing. 2011;15(8):857–870. doi: 10.1007/s00779-011-0401-5

28. Sarker IH, Colman A, Kabir MA, Han J. Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior. The Computer Journal. 2017;61(3):349–368. doi: 10.1093/comjnl/bxx082

29. Sarker IH, Colman A, Han J. RecencyMiner: mining recency-based personalized behavior from contextual smartphone data. Journal of Big Data. 2019;6(1):49. doi: 10.1186/s40537-019-0211-6

30. Carron PM, Kaski K, Dunbar R. Calling Dunbar’s numbers. Social Networks. 2016;47:151–155. https://doi.org/10.1016/j.socnet.2016.06.003.

31. Zerubavel E. Hidden Rhythms: Schedules and Calendars in Social Life. University of California Press; 1985. Available from: https://books.google.com.pk/books?id=cLOKoZH4LB4C.

32. Monsivais D, Bhattacharya K, Ghosh A, Dunbar RIM, Kaski K. Seasonal and geographical impact on human resting periods. Scientific Reports. 2017;7(1):10717. doi: 10.1038/s41598-017-11125-z

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2019 Číslo 10
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