A Study of Mobile Information Exploration with Multi-touch Interactions

  • Shuguang Han
  • I-Han Hsiao
  • Denis Parra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8393)


Compared to desktop interfaces, touch-enabled mobile devices allow richer user interaction with actions such as drag, pinch-in, pinch-out, and swipe. While these actions have been already used to improve the ranking of search results or lists of recommendations, in this paper we focus on understanding how these actions are used in exploration tasks performed over lists of items not sorted by relevance, such as news or social media posts. We conducted a user study on an exploratory task of academic information, and through behavioral analysis we uncovered patterns of actions that reveal user intention to navigate new information, to relocate interesting items already explored, and to analyze details of specific items. With further analysis we found that dragging direction, speed and position all implied users’ judgment on their interests and they offer important signals to eventually learn user preferences.


Multi-touch interactions implicit relevance feedback mobile information seeking behaviors 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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
  2. 2.
    Guo, Q., Jin, H., Lagun, D., Yuan, S., Agichtein, E.: Mining touch interaction data on mobile devices to predict web search result relevance. In: SIGIR 2013, pp. 153–162 (2013)Google Scholar
  3. 3.
    Yi, J., Maghoul, F., Pedersen, J.: Deciphering mobile search patterns: a study of Yahoo! mobile search queries. In: WWW 2008, pp. 257–266. ACM (2008)Google Scholar
  4. 4.
    Kamvar, M., Baluja, S.: Deciphering trends in mobile search. Computer 40(8), 58–62 (2007)CrossRefGoogle Scholar
  5. 5.
    Ricci, F.: Mobile recommender systems. Information Technology & Tourism 12(3), 205–231 (2010)CrossRefGoogle Scholar
  6. 6.
    Brunato, M., Battiti, R.: Pilgrim: A location broker and mobility-aware recommendation system. In: PerCom 2011, pp. 5265–5272. IEEE Computer Society (2003)Google Scholar
  7. 7.
    Dean-Hall, A., Clarke, C., Kamps, J., Thomas, P., Voorhees, E.: Overview of the TREC 2012 Contextual Suggestion Track. In: Proceedings of the 21st NIST Text Retrieval Conference (2012)Google Scholar
  8. 8.
    Huang, J., White, R., Buscher, G., Wang, K.: Improving searcher models using mouse cursor activity. In: SIGIR 2012, pp. 195–204. ACM (2012)Google Scholar
  9. 9.
    Morita, M., Shinoda, Y.: Information filtering based on user behavior analysis and best match text retrieval. In: SIGIR 1994, pp. 272–281 (1994)Google Scholar
  10. 10.
    Kong, W., Aktolga, E., Allan, J.: Improving passage ranking with user behavior information. In: CIKM 2013, pp. 1999–2008. ACM, New York (2013)Google Scholar
  11. 11.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, ICDM 2008. IEEE (2008)Google Scholar
  12. 12.
    Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: Data Mining, ICDM 2008, pp. 502–511 (2008)Google Scholar
  13. 13.
    Manning, C.D., Raghavan, P., Schütze, H.: An Introduction to Information Retrieval, p. 181. Cambridge University Press (2009)Google Scholar
  14. 14.
    Lorigo, L., Haridasan, M., Brynjarsdóttir, H., Xia, L., Joachims, T., Gay, G., Pan, B.: Eye tracking and online search: Lessons learned and challenges ahead. Journal of the American Society for Information Science and Technology 59(7), 1041–1052 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shuguang Han
    • 1
  • I-Han Hsiao
    • 2
  • Denis Parra
    • 3
  1. 1.School of Information SciencesUniversity of PittsburghPittsburghUnited Sates
  2. 2.The EdLab, Teachers CollegeColumbia UniversityNew YorkUnited Sates
  3. 3.School of Computer SciencePUC ChileChile

Personalised recommendations