Inferring Users’ Interest on Web Documents Through Their Implicit Behaviour

  • Stephen AkumaEmail author
  • Chrisina Jayne
  • Rahat Iqbal
  • Faiyaz Doctor
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)


This paper examines the correlation of implicit and explicit user behaviour indicators in a task specific domain. An experiment was conducted and data was collected from 77 undergraduate students of Computer science. Users’ implicit features and explicit ratings of document relevance were captured and logged through a plugin in Firefox browser. A number of implicit indicators were correlated with user explicit ratings and a predictive function model was derived. Classification algorithms were also used to classify documents according to how relevant they are to the current task. It was found that implicit indicators could be used successfully to predict the user rating. These findings can be utilised in building individual and group profile for users of a context-based recommender system.


Implicit indicators Explicit rating Context based Recommender system 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Akuma, S.: Investigating the Effect of Implicit Browsing Behaviour on Students’ Performance in a Task Specific Context. International Journal of Information Technology and Computer Science (IJITCS) 6(5), 11–17 (2014)CrossRefGoogle Scholar
  2. 2.
    Akuma, S., Jayne, C., Iqbal, R., Doctor, F.: Implicit predictive indicators: mouse activity and dwell time. In: Iliadis, L. (ed.) AIAI 2014. IFIP AICT, vol. 436, pp. 162–171. Springer, Heidelberg (2014)Google Scholar
  3. 3.
    Alhindi, A., Kruschwitz, U., Fox, C., Albakour, M.: Profile-Based Summarisation for Web Site Navigation. ACM Transactions on Information Systems 33(1), 1–40 (2015)CrossRefGoogle Scholar
  4. 4.
    Balakrishnan, V., Zhang, X.: Implicit user behaviours to improve post-retrieval document relevancy. Computers in Human Behavior 33, 104–112 (2014)CrossRefGoogle Scholar
  5. 5.
    Borlund, P.: The IIR evaluation model: A framework for evaluation of interactive information retrieval systems. Information Research 8(3) (2003)Google Scholar
  6. 6.
    Claypool, M., Le, P., Wased, M., Brown, D.: Implicit interest indicators. In: Proceedings of the International Conference on Intelligent User Interfaces, IUI, pp. 33–40 (2001)Google Scholar
  7. 7.
    Guo, Q., Agichtein, E.: Beyond dwell time: estimating document relevance from cursor movements and other post-click searcher behavior. In: WWW 2012 - Proceedings of the 21st Annual Conference on World Wide Web, pp. 569–578 (2012)Google Scholar
  8. 8.
    Liu, J., Liu, C., Belkin, N.: Examining the effects of task topic familiarity on searchers’ behaviors in different task types. In: Proceedings of the ASIST Annual Meeting, vol. 50(1) (2013)Google Scholar
  9. 9.
    Morita, M., Shinoda, Y.: Information filtering based on user behaviour analysis and best matchtext retrieval. In: Proceedings of SIGIR Conference on Research and Development, pp. 272–281 (1994)Google Scholar
  10. 10.
    Nichols, D.M.: Implicit ratings and riltering. In: Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering, Budapaest, Hungary, pp. 10–12 (1997)Google Scholar
  11. 11.
    Núñez-Valdéz, E.R., Cueva Lovelle, J.M., Sanjuán, O., García-Díaz, V., De Pablos, P.O., Montenegro Marín, C.E.: Implicit feedback techniques on recommender systems applied to electronic books. Computers in Human Behavior 28(4), 1186–1193 (2012)CrossRefGoogle Scholar
  12. 12.
    Oard, D., Kim, J.: Implicit feedback for recommendation systems. In: Proceedings of the AAAI Workshop on Recommender Systems, pp. 81–83 (1998)Google Scholar
  13. 13.
    Teevan, J., Dumais, S.T., Horvitz, E.: Potential for personalization. ACM Transactions on Computer-Human Interaction 17(1) (2010)Google Scholar
  14. 14.
    White, R.W., Kelly, D.: A study on the effects of personalization and task information on implicit feedback performance. In: Proceedings of the International Conference on Information and Knowledge Management, pp. 297–306 (2006)Google Scholar
  15. 15.
    Zemirli, N.: WebCap: inferring the user’s interests based on a real-time implicit feedback. In: 7th International Conference on Digital Information Management, ICDIM 2012, pp. 62–67 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Stephen Akuma
    • 1
    Email author
  • Chrisina Jayne
    • 1
  • Rahat Iqbal
    • 1
  • Faiyaz Doctor
    • 1
  1. 1.Department of Computing and the Digital EnvironmentCoventry UniversityCoventryUK

Personalised recommendations