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International Conference on Flexible Query Answering Systems

FQAS 2021: Flexible Query Answering Systems pp 223–234Cite as

The Impact of User Demographics and Task Types on Cross-App Mobile Search

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 12871)

Abstract

Recent developments in the mobile app industry have resulted in various types of mobile apps, each targeting a different need and a specific audience. Consequently, users access distinct apps to complete their information need tasks. This leads to the use of various apps not only separately, but also collaboratively in the same session to achieve a single goal. Recent work has argued the need for a unified mobile search system that would act as metasearch on users’ mobile devices. The system would identify the target apps for the user’s query, submit the query to the apps, and present the results to the user in a unified way. In this work, we aim to deepen our understanding of user behavior while accessing information on their mobile phones by conducting an extensive analysis of various aspects related to the search process. In particular, we study the effect of task type and user demographics on their behavior in interacting with mobile apps. Our findings reveal trends and patterns that can inform the design of a more effective mobile information access environment.

Keywords

  • Mobile search
  • User evaluation

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  • DOI: 10.1007/978-3-030-86967-0_17
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Notes

  1. 1.

    https://github.com/aliannejadi/unimobile.

  2. 2.

    Estimated via empirical analysis of a Bootstrap sampling with 1,000 resamples.

References

  1. Mobile vs. desktop internet usage (latest 2020 data). https://www.broadbandsearch.net/blog/mobile-desktop-internet-usage-statistics#

  2. Aliannejadi, M., Harvey, M., Costa, L., Pointon, M., Crestani, F.: Understanding mobile search task relevance and user behaviour in context. In: CHIIR, pp. 143–151. ACM (2019)

    Google Scholar 

  3. Aliannejadi, M., Zamani, H., Crestani, F., Croft, W.B.: In situ and context-aware target apps selection for unified mobile search. In: CIKM, pp. 1383–1392. ACM (2018)

    Google Scholar 

  4. Aliannejadi, M., Zamani, H., Crestani, F., Croft, W.B.: Target apps selection: towards a unified search framework for mobile devices. In: SIGIR, pp. 215–224. ACM (2018)

    Google Scholar 

  5. Aliannejadi, M., Zamani, H., Crestani, F., Croft, W.B.: Context-aware target apps selection and recommendation for enhancing personal mobile assistants. CoRR abs/2101.03394 (2021)

    Google Scholar 

  6. Borlund, P.: The concept of relevance in IR. J. Am. Soc. Inform. Sci. Technol. 54(10), 913–925 (2003)

    CrossRef  Google Scholar 

  7. Carrascal, J.P., Church, K.: An in-situ study of mobile app & mobile search interactions. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 2739–2748 (2015)

    Google Scholar 

  8. Costa, L., Aliannejadi, M., Crestani, F.: A tool for conducting user studies on mobile devices. In: CHIIR, pp. 462–466. ACM (2020)

    Google Scholar 

  9. Crestani, F., Du, H.: Written versus spoken queries: a qualitative and quantitative comparative analysis. JASIST 57(7), 881–890 (2006)

    CrossRef  Google Scholar 

  10. Crestani, F., Mizzaro, S., Scagnetto, I.: Mobile Information Retrieval. Springer Briefs in Computer Science, Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60777-1

    CrossRef  Google Scholar 

  11. Domingo, M.G., Garganté, A.B.: Exploring the use of educational technology in primary education: teachers’ perception of mobile technology learning impacts and applications’ use in the classroom. Comput. Hum. Behav. 56, 21–28 (2016)

    CrossRef  Google Scholar 

  12. Gordon, M.L., Gatys, L., Guestrin, C., Bigham, J.P., Trister, A., Patel, K.: App usage predicts cognitive ability in older adults. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2019)

    Google Scholar 

  13. Guy, I.: Searching by talking: analysis of voice queries on mobile web search. In: SIGIR, pp. 35–44 (2016)

    Google Scholar 

  14. Hargittai, E., Piper, A.M., Morris, M.R.: From internet access to internet skills: digital inequality among older adults. Univ. Access Inf. Soc. 18(4), 881–890 (2018). https://doi.org/10.1007/s10209-018-0617-5

    CrossRef  Google Scholar 

  15. Harvey, M., Pointon, M.: Searching on the go: the effects of fragmented attention on mobile web search tasks. In: SIGIR, pp. 155–164. ACM (2017)

    Google Scholar 

  16. Hinds, J., Joinson, A.N.: What demographic attributes do our digital footprints reveal? A systematic review. PLoS ONE 13(11), e0207112 (2018)

    CrossRef  Google Scholar 

  17. Johnson, J.: Daily time spent online by device 2021, January 2021. https://www.statista.com/statistics/319732/daily-time-spent-online-device/

  18. Kamvar, M., Baluja, S.: A large scale study of wireless search behavior: google mobile search. In: CHI, pp. 701–709 (2006)

    Google Scholar 

  19. Karatzoglou, A., Baltrunas, L., Church, K., Böhmer, M.: Climbing the app wall: enabling mobile app discovery through context-aware recommendations. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 2527–2530 (2012)

    Google Scholar 

  20. Krismayer, T., Schedl, M., Knees, P., Rabiser, R.: Predicting user demographics from music listening information. Multimed. Tools Appl. 78(3), 2897–2920 (2018). https://doi.org/10.1007/s11042-018-5980-y

    CrossRef  Google Scholar 

  21. Liu, B., Wu, Y., Gong, N.Z., Wu, J., Xiong, H., Ester, M.: Structural analysis of user choices for mobile app recommendation. ACM Trans. Knowl. Discov. Data (TKDD) 11(2), 1–23 (2016)

    CrossRef  Google Scholar 

  22. Malmi, E., Weber, I.: You are what apps you use: demographic prediction based on user’s apps. In: ICWSM, pp. 635–638. AAAI Press (2016)

    Google Scholar 

  23. Murgia, E., Landoni, M., Huibers, T., Fails, J.A., Pera, M.S.: The seven layers of complexity of recommender systems for children in educational contexts. In: Proceedings of the 2019 ComplexRec Workshop: Co-Located with the 13th ACM Conference on Recommender Systems (2019). http://ceur-ws.org/Vol-2449/paper1.pdf

  24. Ong, K., Järvelin, K., Sanderson, M., Scholer, F.: Using information scent to understand mobile and desktop web search behavior. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 295–304 (2017)

    Google Scholar 

  25. Pandey, A., Hasan, S., Dubey, D., Sarangi, S.: Smartphone apps as a source of cancer information: changing trends in health information-seeking behavior. J. Cancer Educ. 28(1), 138–142 (2013)

    CrossRef  Google Scholar 

  26. Peng, M., Zeng, G., Sun, Z., Huang, J., Wang, H., Tian, G.: Personalized app recommendation based on app permissions. World Wide Web 21(1), 89–104 (2017). https://doi.org/10.1007/s11280-017-0456-y

    CrossRef  Google Scholar 

  27. Pera, M.S., Murgia, E., Landoni, M., Huibers, T.: With a little help from my friends: use of recommendations at school. In: Proceedings of ACM RecSys 2019 Late-Breaking Results: Co-Located with the 13th ACM Conference on Recommender Systems, pp. 61–65 (2019)

    Google Scholar 

  28. Rosales, A., Fernández-Ardèvol, M.: Smartphone usage diversity among older people. In: Sayago, S. (ed.) Perspectives on Human-Computer Interaction Research with Older People. HIS, pp. 51–66. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-06076-3_4

    CrossRef  Google Scholar 

  29. Saccomani, P.: People spent 90% of their mobile time using apps in 2019, February 2021. https://www.mobiloud.com/blog/mobile-apps-vs-the-mobile-web

  30. Song, Y., Ma, H., Wang, H., Wang, K.: Exploring and exploiting user search behavior on mobile and tablet devices to improve search relevance. In: WWW, pp. 1201–1212 (2013)

    Google Scholar 

  31. Starkey, L., Eppel, E.A., Sylvester, A.: How do 10-year-old New Zealanders participate in a digital world? Inf. Commun. Soc. 22(13), 1929–1944 (2019)

    CrossRef  Google Scholar 

  32. Tian, Y., Zhou, K., Lalmas, M., Pelleg, D.: Identifying tasks from mobile app usage patterns. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2357–2366 (2020)

    Google Scholar 

  33. Wai, I.S.H., Ng, S.S.Y., Chiu, D.K., Ho, K.K., Lo, P.: Exploring undergraduate students’ usage pattern of mobile apps for education. J. Librariansh. Inf. Sci. 50(1), 34–47 (2018)

    CrossRef  Google Scholar 

  34. Wang, C., Zheng, Y., Jiang, J., Ren, K.: Toward privacy-preserving personalized recommendation services. Engineering 4(1), 21–28 (2018)

    CrossRef  Google Scholar 

  35. Wang, Y., Xiao, Y., Ma, C., Xiao, Z.: Improving users’ demographic prediction via the videos they talk about. In: EMNLP, pp. 1359–1368. The Association for Computational Linguistics (2016)

    Google Scholar 

  36. Weber, I., Castillo, C.: The demographics of web search. In: SIGIR, pp. 523–530. ACM (2010)

    Google Scholar 

  37. Wildemuth, B., Freund, L., Toms, E.G.: Untangling search task complexity and difficulty in the context of interactive information retrieval studies. J. Doc. 70, 23 (2014)

    CrossRef  Google Scholar 

  38. Zhang, A., et al.: Towards mobile query auto-completion: an efficient mobile application-aware approach. In: Proceedings of the 25th International Conference on World Wide Web, pp. 579–590 (2016)

    Google Scholar 

  39. Zhong, E., Tan, B., Mo, K., Yang, Q.: User demographics prediction based on mobile data. Pervasive Mob. Comput. 9(6), 823–837 (2013)

    CrossRef  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Mohammad Aliannejadi

  2. Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani & Monica Landoni

  3. University of Twente, Enschede, The Netherlands

    Theo Huibers

  4. Università degli Studi di Milano-Bicocca Milano, Milan, Italy

    Emiliana Murgia

  5. Boise State University, Boise, ID, USA

    Maria Soledad Pera

Authors
  1. Mohammad Aliannejadi
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  2. Fabio Crestani
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  3. Theo Huibers
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  4. Monica Landoni
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  5. Emiliana Murgia
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  6. Maria Soledad Pera
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Corresponding author

Correspondence to Fabio Crestani .

Editor information

Editors and Affiliations

  1. Roskilde University, Roskilde, Denmark

    Troels Andreasen

  2. Ghent University, Ghent, Belgium

    Guy De Tré

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Prof. Janusz Kacprzyk

  4. Legind Technologies ⅍, Esbjerg, Denmark

    Prof. Dr. Henrik Legind Larsen

  5. CNR - IREA, Milan, Italy

    Gloria Bordogna

  6. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Sławomir Zadrożny

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Aliannejadi, M., Crestani, F., Huibers, T., Landoni, M., Murgia, E., Pera, M.S. (2021). The Impact of User Demographics and Task Types on Cross-App Mobile Search. In: Andreasen, T., De Tré, G., Kacprzyk, J., Legind Larsen, H., Bordogna, G., Zadrożny, S. (eds) Flexible Query Answering Systems. FQAS 2021. Lecture Notes in Computer Science(), vol 12871. Springer, Cham. https://doi.org/10.1007/978-3-030-86967-0_17

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  • DOI: https://doi.org/10.1007/978-3-030-86967-0_17

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