Skip to main content
SpringerLink
Log in

Tuning user profiles based on analyzing dynamic preference in document retrieval systems

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Modeling users’ preferences and needs is one of the most important personalization tasks in information retrieval domain. In this paper a model for user profile tuning in document retrieval systems is considered. Methods for tuning the user profile based on analysis of user preferences dynamics are experimentally evaluated to check whether with growing history of user activity the created user profile can converge to his preferences. As the statistical analysis of series of simulations has shown, proposed methods of user profile actualization are effective in the sense that the distance between user preferences and his profile becomes smaller and smaller along with time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Arapakis I, Athanasakos K, Jose J (2010) A comparison of general vs personalised affective models for the prediction of topical relevance. ACM SIGIR10:371–378

    Google Scholar 

  2. Bhogal J, Macfarlane A, Smith P (2007) A review of ontology based query expansion. Inf Process Manag 43:866–886

    Article  Google Scholar 

  3. Clarkea CLA, Cormackb G, Tudhope EA (2000) Relevance ranking for one to three term queries. Inf Process Manag 36:291–311

    Article  Google Scholar 

  4. Duong TH, Jo GS, Jung JJ, Nguyen NT (2009) Complexity analysis of ontology integration methodologies: a comparative study. J Univers Comput Sci 15(4):877–897

    MathSciNet  MATH  Google Scholar 

  5. Esposito F, Semeraro G, Ferilli S, Degemmis M, Di Mauro N, Basile TMA, Lops P (2003) Evaluation and validation of two approaches to user profiling. In: Proceedings of EWMF’, vol 03, pp 130–147

  6. Godoy D, Amandi A (2005) User profiling in personal information agents: a survey. Knowl Eng Rev 20:329–361

    Article  Google Scholar 

  7. Hider P (2006) Search goal revision in models of information retrieval. J Inf Sci 32(4):352–361

    Article  Google Scholar 

  8. Indyka-Piasecka A (2006) User Model in Internet Information Retrieval Systems. PhD Dissertation, Wroclaw University of Technology

  9. Jones KS (1999) Information retrieval and artificial intelligence. Artif Intell 114:257–281

    Article  MATH  Google Scholar 

  10. Jung JJ (2008) Ontology-based context synchronization for ad hoc social collaborations. Knowl-Based Syst 21(7):573–580

    Article  Google Scholar 

  11. Jung JJ (2011) Boosting social collaborations based on contextual synchronization: an empirical study. Expert Syst Appl 38(5):4809–4815

    Article  Google Scholar 

  12. Jung JJ (2012) Evolutionary approach for semantic-based query sampling in large-scale information sources. Inf Sci 182(1):30–39

    Article  Google Scholar 

  13. Manning ChD, Raghavan P, Schütze H (2009) Introduction to information retrieval. Cambridge University Press

  14. Mianowska B (2012) Algorithms for user profile tuning based on user preferences in documents retrieval systems(description of implementation). Technical Report no. SPR 1/2012. Wroclaw University of Technology

  15. Mianowska B, Nguyen NT (2011) A method of user modeling and relevance simulation in document retrieval systems. In: Proceedings of KES-AMSTA 2011. LNAI, vol 6682, pp 138–147

  16. Mianowska B, Nguyen NT (2012) A method for tuning user profiles based on analysis of user preference dynamics in document retrieval systems. In: Proceedings of ICAISC 2012. LNCS, vol 7267, pp 673–681

  17. Mianowska B, Nguyen NT (2011) A method of user profile adaptation in document retrieval. In: Proceedings of ACIIDS 2011. LNAI, vol 6592, pp 181–192

  18. Mianowska B, Nguyen NT (2011) Using knowledge integration techniques for user profile adaptation method in document retrieval systems. T Computational Collective Intelligence V. LNCS 6910:140–156

    Article  Google Scholar 

  19. Mostafa J, Mukhopadhyay S, Lam W, Palakal M (1997) A multilevel approach to intelligent information filtering: model, system, and evaluation. ACM Trans Inf Sys 15(4):368–399

    Article  Google Scholar 

  20. Nanas N, Uren V, De Roeck A (2004) A comparative study of term weighting methods for information filtering. In: Proceedings of the 15th International Workshop on Database and Expert Systems Applications DEXA’04 (13–17) IEEE Computer Society

  21. Nguyen NT (2006) Conflicts of ontologies – classification and consensus-based methods for resolving. In: Proceedings of KES 2006. LNAI, vol 4252, 267–274

  22. Nie J (1988) An outline of a general model for information retrieval systems. In: Proceedings of the 11th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM New York, NY, USA

    Google Scholar 

  23. Pazzani M, Billsus D (1997) Learning and revising user profiles: the identification of interesting web sites. Mach Learn 27(3):313–331

    Article  Google Scholar 

  24. Ren F, Bracewell DB (2009) Advanced information retrieval. Electron Notes Theor Comput Sci 225:303–317

    Article  Google Scholar 

  25. Rijsbergen CJ (1979) Information retrieval. Information Retrieval Group, University of Glasgow

  26. Salton G, Buckley Ch (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24(5):513–523

    Article  Google Scholar 

  27. Shen X, Tan B, Zhai ChX (2005) Implicit user modeling for personalized search. ACM CIKM05:824–831

    Google Scholar 

  28. Stojanovic N (2005) On the role of a user’s knowledge gap in an information retrieval process. ACM K-CAP05:83–90

  29. Syed HH, Andritsos P (2007) User preference modeling - a survey report. Technical Report #DIT-07-060. University of Trento

  30. Tan AH, Teo C (1998) Learning user profiles for personalized information dissemination. IEEE International Joint Conference on Neural Networks Proceedings IEEE World Congress on Computational Intelligence Cat 98(36227):183–188

    Google Scholar 

  31. Tang J, Yao L, Zhang D, Zhang J (2010) A combination approach to web user profiling. ACM T Knowl Disc Data 5(1):2. doi:10.1145/1870096.1870098

    Article  Google Scholar 

  32. Vallet DJ (2007) Personalized information retrieval in context using ontological knowledge. Advanced Studies Diploma – EPS-UAM

  33. Wang YD, Forgionne G (2008) Testing a decision-theoretic approach to the evaluation of information retrieval systems. J Inf Sci 34(6):861–876

    Article  Google Scholar 

  34. Widyantoro DH (1999) Dynamic modeling and learning user profile in personalized news agent. Master Thesis, Texas M&A University

Download references

Acknowledgements

This research was partially supported by Polish Ministry of Science and Higher Education under grant no. N N519 407437 (2009-2012).

Author information

Authors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Bernadetta Mianowska & Ngoc Thanh Nguyen

Authors
  1. Bernadetta Mianowska
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ngoc Thanh Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bernadetta Mianowska.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mianowska, B., Nguyen, N.T. Tuning user profiles based on analyzing dynamic preference in document retrieval systems. Multimed Tools Appl 65, 93–118 (2013). https://doi.org/10.1007/s11042-012-1145-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-012-1145-6

Keywords

Search

Navigation