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Investigating smartphone user differences in their application usage behaviors: an empirical study

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Abstract

Smartphone applications (Abbr. apps) have become an indispensable part in our everyday lives. Users determine what apps to use depending on their personal needs and interests. Users with different attributes may have different needs, making it natural for their app usage behaviors to be different. The differences in app usage behaviors among users make it possible to infer their attributes. Knowing such differences could help improve mobile user experiences by enhancing smart services and devices. In this paper, we present an empirical study of investigating smartphone user differences on a large-scale dataset of recently used app lists from 106,672 Android users from China. We first investigate the user differences in app usage behaviors with respect to their attributes (gender, age, and income level). We find significant differences in app usage frequency, app usage with time context and functions. We then extract corresponding features from app usage records to infer the attributes of each user, and investigate the predictive ability of individual features and combinations of different individual features. We achieve the accuracy of 83.29% for gender, 69.94% for age (four age ranges) and 71.43% for income level (three income levels) with the best set of features, respectively. Finally, we discuss the implications of our findings and the limitations of this work.

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Notes

  1. https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/.

  2. https://www.statista.com/statistics/276623/number-of-apps-available-in-leading-app-stores/.

References

  • Andone, I., Błaszkiewicz, K., Eibes, M., Trendafilov, B., Montag, C., Markowetz, A.: How age and gender affect smartphone usage. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, pp. 9–12. ACM (2016)

  • Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  • Böhmer, M., Hecht, B., Schöning, J., Krüger, A., Bauer, G.: Falling asleep with angry birds, facebook and kindle: a large scale study on mobile application usage. In: Proceedings of the 13th international conference on Human computer interaction with mobile devices and services, pp. 47–56. ACM (2011)

  • Brdar, S., Culibrk, D., Crnojevic, V.: Demographic attributes prediction on the real-world mobile data. In: 2012 Nokia Mobile Data Challenge Workshop (2012)

  • Cao, H., Lin, M.: Mining smartphone data for app usage prediction and recommendations: a survey. Pervasive Mob. Comput. 37, 1–22 (2017)

    Article  Google Scholar 

  • Chittaranjan, G., Blom, J., Gatica-Perez, D.: Who’s who with big-five: Analyzing and classifying personality traits with smartphones. In: 2011 15th Annual International Symposium on Wearable Computers (ISWC), pp. 29–36. IEEE (2011)

  • Chittaranjan, G., Blom, J., Gatica-Perez, D.: Mining large-scale smartphone data for personality studies. Pers. Ubiquitous Comput. 17(3), 433–450 (2013)

    Article  Google Scholar 

  • De Bock, K., Van den Poel, D.: Predicting website audience demographics for web advertising targeting using multi-website click stream data. Fund. Inf. 98(1), 49–70 (2010)

    Google Scholar 

  • Fast, L.A., Funder, D.C.: Personality as manifest in word use: correlations with self-report, acquaintance report, and behavior. J. Personal. Soc. Psychol. 94(2), 334 (2008)

    Article  Google Scholar 

  • Frey, R., Xu, R., Ilic, A.: Reality-mining with smartphones: detecting and predicting life events based on app installation behavior (2015)

  • Frey, R.M., Xu, R., Ilic, A.: Mobile app adoption in different life stages: an empirical analysis. Pervasive Mob. Comput. 40, 512–527 (2017)

    Article  Google Scholar 

  • He, X., Pan, J., Jin, O., Xu, T., Liu, B., Xu, T., Shi, Y., Atallah, A., Herbrich, R., Bowers, S., et al.: Practical lessons from predicting clicks on ads at facebook. In: ADKDD2014, pp. 1–9. ACM (2014)

  • Herring, S.C., Paolillo, J.C.: Gender and genre variation in weblogs. J. Socioling. 10(4), 439–459 (2006)

    Article  Google Scholar 

  • Hosmer Jr., D.W., Lemeshow, S., Sturdivant, R.X.: Applied logistic regression, vol. 398. Wiley, New York (2013)

    Book  MATH  Google Scholar 

  • Jesdabodi, C., Maalej, W.: Understanding usage states on mobile devices. In: UbiComp2015, pp. 1221–1225. ACM (2015)

  • John Lu, Z.: The elements of statistical learning: data mining, inference, and prediction. J. Royal Stat. Soc. Series A (Stat. Soc.) 173(3), 693–694 (2010)

    Article  Google Scholar 

  • Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: ICML2014, pp. 1188–1196 (2014)

  • LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)

    Article  Google Scholar 

  • Li, H., Lu, X.: Mining device-specific apps usage patterns from large-scale android users. arXiv preprint arXiv:1707.09252 (2017)

  • Li, H., Lu, X., Liu, X., Xie, T., Bian, K., Lin, F.X., Mei, Q., Feng, F.: Characterizing smartphone usage patterns from millions of android users. In: Proceedings of the 2015 Internet Measurement Conference, pp. 459–472. ACM (2015a)

  • Li, S., Wang, J., Zhou, G., Shi, H.: Interactive gender inference with integer linear programming. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 2341–2347 (2015b)

  • Liu, X., Li, H., Lu, X., Xie, T., Mei, Q., Feng, F., Mei, H.: Understanding diverse usage patterns from large-scale appstore-service profiles. IEEE Trans. Softw. Eng. 44(4), 384–411 (2018)

    Article  Google Scholar 

  • Mairesse, F., Walker, M.A., Mehl, M.R., Moore, R.K.: Using linguistic cues for the automatic recognition of personality in conversation and text. J. Artif. Intell. Res. 30, 457–500 (2007)

    Article  MATH  Google Scholar 

  • Malmi, E., Weber, I.: you are what apps you use: demographic prediction based on user’s apps. arXiv preprint arXiv:1603.00059 (2016)

  • Mo, K., Tan, B., Zhong, E., Yang, Q.: Report of task 3: your phone understands you. In: 2012 Nokia Mobile Data Challenge Workshop, pp. 18–19. Citeseer (2012)

  • Ouyang, Y., Guo, B., Guo, T., Cao, L., Yu, Z.: Modeling and forecasting the popularity evolution of mobile apps: a multivariate hawkes process approach. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2(4), 182 (2018)

    Article  Google Scholar 

  • Peltonen, E., Lagerspetz, E., Hamberg, J., Mehrotra, A., Musolesi, M., Nurmi, P., Tarkoma, S.: The hidden image of mobile apps: Geographic, demographic, and cultural factors in mobile usage (2018)

  • Qin, Z., Wang, Y., Cheng, H., Zhou, Y., Sheng, Z., Leung, V.C.M.: Demographic information prediction: a portrait of smartphone application users. IEEE Trans. Emerg. Top. Comput. 6(3), 432–444 (2016)

    Article  Google Scholar 

  • Rivron, V., Khan, M.I., Charneau, S., Chrisment, I.: Exploring smartphone application usage logs with declared sociological information. In: 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom)(BDCloud-SocialCom-SustainCom), pp. 266–273. IEEE (2016)

  • Seneviratne, S., Seneviratne, A., Mohapatra, P., Mahanti, A.: Your installed apps reveal your gender and more!. ACM SIGMOBILE Mob. Comput. Commun. Rev. 18(3), 55–61 (2015)

    Article  Google Scholar 

  • Tu, Z., Fan, Y., Li, Y., Chen, X., Su, L., Jin, D.: From fingerprint to footprint: cold-start location recommendation by learning user interest from app data. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(1), 26 (2019)

    Article  Google Scholar 

  • Wang, Y., Tang, Y., Ma, J., Qin, Z.: Gender prediction based on data streams of smartphone applications. In: Proceedings of International Conference on Big Data Computing and Communications, pp. 115–125. Springer (2015)

  • Wang, J., Wang, L., Wang, Y., Zhang, D., Kong, L.: Task allocation in mobile crowd sensing: state-of-the-art and future opportunities. IEEE Internet Things J 5(5), 3747–3757 (2018)

    Article  Google Scholar 

  • Wang, J., Wang, Y., Lv, Q.: Crowd-assisted machine learning: current issues and future directions. Computer 52(1), 46–53 (2019)

    Article  Google Scholar 

  • Wei, X., Huang, H., Nie, L., Zhang, H., Mao, X.L., Chua, T.S.: I know what you want to express: sentence element inference by incorporating external knowledge base. IEEE Trans. Knowl. Data Eng. 29(2), 344–358 (2017)

    Article  Google Scholar 

  • Xu, Q., Erman, J., Gerber, A., Mao, Z., Pang, J., Venkataraman, S.: Identifying diverse usage behaviors of smartphone apps. In: IMC2011, pp. 329–344. ACM (2011)

  • Xu, R., Frey, R.M., Fleisch, E., Ilic, A.: Understanding the impact of personality traits on mobile app adoption-insights from a large-scale field study. Comput. Hum. Behav. 62, 244–256 (2016a)

    Article  Google Scholar 

  • Xu, R., Frey, R.M., Ilic, A.: Individual differences and mobile service adoption: An empirical analysis. In: Proceedings of IEEE Second International Conference on Big Data Computing Service and Applications, pp. 234–243. IEEE (2016b)

  • Ying, J.J.C., Chang, Y.J., Huang, C.M., Tseng, V.S.: Demographic prediction based on users mobile behaviors. 2012 Nokia Mobile Data Challenge Workshop pp. 1–6 (2012)

  • Yu, D., Li, Y., Xu, F., Zhang, P., Kostakos, V.: Smartphone app usage prediction using points of interest. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1(4), 174 (2018)

    Article  Google Scholar 

  • Yu, Z., Du, H., Yi, F., Wang, Z., Guo, B.: Ten scientific problems in human behavior understanding. CCF Trans. Pervasive Comput. Interact. 1(1), 3–9 (2019)

    Article  Google Scholar 

  • Zhao, S., Ramos, J., Tao, J., Jiang, Z., Li, S., Wu, Z., Pan, G., Dey, A.K.: Discovering different kinds of smartphone users through their application usage behaviors. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 498–509. ACM (2016)

  • Zhao, S., Pan, G., Zhao, Y., Tao, J., Chen, J., Li, S., Wu, Z.: Mining user attributes using large-scale app lists of smartphones. IEEE Syst. J. 11(1), 315–323 (2017a)

    Article  Google Scholar 

  • Zhao, S., Ramos, J., Tao, J., Jiang, Z., Li, S., Wu, Z., Pan, G., Dey, A.K.: Who are the smartphone users? Identifying user groups with apps usage behaviors. GetMobile Mob. Comput. Commun. 21(2), 31–34 (2017b)

    Article  Google Scholar 

  • Zhao, S., Zhao, Y., Zhao, Z., Luo, Z., Huang, R., Li, S., Pan, G.: Characterizing a user from large-scale smartphone-sensed data. In: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing (workshop), pp. 482–487. ACM (2017c)

  • Zhao, S., Xu, F., Luo, Z., Li, S., Pan, G.: Demographic attributes prediction through app usage behaviors on smartphones. In: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, pp. 870–877. ACM (2018)

  • Zhao, S., Jiang, Z., Ramos, J., Luo, Z., Li, S., Dey, K.A., Pan, G.: User profiling from their use of smartphone applications: a survey. Pervasive and Mobile Comput. https://doi.org/10.1016/j.pmcj.2019.101052 (2019a)

    Google Scholar 

  • Zhao, S., Luo, Z., Jiang, Z., Wang, H., Xu, F., Li, S., Yin, J., Pan, G.: Appusage2vec: Modeling smartphone app usage for prediction. In: Proceedings of the 35th IEEE International Conference on Data Engineering. IEEE (2019b)

  • Zhao, S., Zhao, Z., Huang, R., Luo, Z., Li, S., Tao, J., Cheng, S., Fan, J., Pan, G.: Discovering individual life style from anonymized wifi scan lists on smartphones. IEEE Access 7, 22698–22709 (2019c)

    Article  Google Scholar 

  • Zou, X., Zhang, W., Li, S., Pan, G.: Prophet: What app you wish to use next. In: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication (poster), pp. 167–170. ACM (2013)

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Acknowledgements

This work was supported by National Key R&D Program of China (2018YFC1504006), NSF of China (Nos. 61802340, 61772460, and 61802342), China Postdoctoral Science Foundation under Grant Nos. 2017M620246 and 2018T110591, and the Fundamental Research Funds for the Central Universities (No. 181200*172210183).

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Authors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Sha Zhao, Feng Xu, Yizhi Xu, Zhiling Luo, Shijian Li & Gang Pan

  2. Hong Kong University of Science and Technology, Hong Kong, China

    Xiaojuan Ma

  3. University of Washington, Seattle, USA

    Anind Dey

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  1. Sha Zhao
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  2. Feng Xu
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  3. Yizhi Xu
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  8. Gang Pan
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Correspondence to Gang Pan.

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Zhao, S., Xu, F., Xu, Y. et al. Investigating smartphone user differences in their application usage behaviors: an empirical study. CCF Trans. Pervasive Comp. Interact. 1, 140–161 (2019). https://doi.org/10.1007/s42486-019-00011-4

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