1. Hannak, A., Sapiezynski, P., Molavi Kakhki, A., Krishnamurthy, B., Lazer, D., Mislove, A., Wilson, C.: Measuring personalization of web search. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 527–538 (2013)

   Google Scholar 

2. Smit, E.G., Van Noort, G., Voorveld, H.A.: Understanding online behavioural advertising: User knowledge, privacy concerns and online coping behaviour in europe. Computers in Human Behavior32, 15–22 (2014)

   Article  Google Scholar 

3. Jansen, B.J., Moore, K., Carman, S.: Evaluating the performance of demographic targeting using gender in sponsored search. Information Processing & Management49(1) (2013)

   Google Scholar 

4. Yan, X., Yan, L.: Gender classification of weblog authors. In: AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, pp. 228–230 (2006)

   Google Scholar 

5. Garera, N., Yarowsky, D.: Modeling latent biographic attributes in conversational genres. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 2, pp. 710–718. Association for Computational Linguistics (2009)

   Google Scholar 

6. Kabbur, S., Han, E.H., Karypis, G.: Content-based methods for predicting web-site demographic attributes. In: 2010 IEEE 10th International Conference on Data Mining (ICDM), pp. 863–868. IEEE (2010)

   Google Scholar 

7. Weber, I., Jaimes, A.: Demographic information flows. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. ACM (2010)

   Google Scholar 

8. Ying, J.J.C., Chang, Y.J., Huang, C.M., Tseng, V.S.: Demographic prediction based on users mobile behaviors. Mobile Data Challenge (2012)

   Google Scholar 

9. Seneviratne, S., Seneviratne, A., Mohapatra, P., Mahanti, A.: Predicting user traits from a snapshot of apps installed on a smartphone. ACM SIGMOBILE Mobile Computing and Communications Review18(2), 1–8 (2014)

   Article  Google Scholar 

10. Qin, Z., Wang, Y., Xia, Y., Cheng, H., Zhou, Y., Sheng, Z., Leung, V.: Demographic information prediction based on smartphone application usage. In: 2014 International Conference on Smart Computing (SMARTCOMP), pp. 183–190. IEEE (2014)

    Google Scholar 

11. Laurila, J.K., Gatica-Perez, D., Aad, I., Bornet, O., Do, T.M.T., Dousse, O., Eberle, J., Miettinen, M., et al.: The mobile data challenge: big data for mobile computing research. In: Pervasive Computing. Number EPFL-CONF-192489 (2012)

    Google Scholar 

12. Hamka, F., Bouwman, H., De Reuver, M., Kroesen, M.: Mobile customer segmentation based on smartphone measurement. Telematics and Informatics31(2), 220–227 (2014)

    Article  Google Scholar 

13. Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences110(15), 5802–5805 (2013)

    Article  Google Scholar 

14. Au, K.W.Y., Zhou, Y.F., Huang, Z., Gill, P., Lie, D.: Short paper: a look at smartphone permission models. In: Proceedings of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices, pp. 63–68. ACM (2011)

    Google Scholar 

15. Deng, N., Tian, Y., Zhang, C.: Support vector machines: optimization based theory, algorithms, and extensions. CRC Press (2012)

    Google Scholar 

16. Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, vol. 14, pp. 1137–1145 (1995)

    Google Scholar 

17. Hu, J., Zeng, H.J., Li, H., Niu, C., Chen, Z.: Demographic prediction based on user’s browsing behavior. In: Proceedings of the 16th International Conference on World Wide Web, pp. 151–160. ACM (2007)

    Google Scholar 

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