Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Online and interactive self-adaptive learning of user profile using incremental evolutionary algorithms

Abstract

In this contribution, we explore the application of evolutionary algorithms for information filtering. There are two crucial issues we consider in this study: (1) the generation of the user’s profile which is the central task of any information filtering or routing system; (2) self-adaptation and self-evolving of the user’s profile given the dynamic nature of information filtering. Basically the problem is to find the set of weighted terms that best describe the interests of the user. Thus, the problem of user profile generation can be perceived as an optimization problem. Moreover, because the user’s interests are obtained implicitly and continuously over time from the relevance feedback of the user, the optimization process must be incremental and interactive. To meet these requirements, an incremental evolutionary algorithm that updates the profile over time as new feedback becomes available is introduced. New genetic operators (crossover and mutation) fitting the application at hand are proposed. Moreover, methods for feature selection, incremental update of the profile and multi-profiling are devised. The experimental investigations show that the proposed approach including the individual methods for the different aspects is suitable and provides high performance rates on real-world data sets.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Notes

  1. 1.

    In the context of this study by ”information filtering” (IF) we refer to “text filtering” (TF) and we will use them interchangeably

References

  1. Algarni A, Li Y, Xu Y (2010) Selected new training documents to update user profile. In: CIKM, pp 799–808

  2. Borji A, Jahromi M (2008) Evolving weighting functions for query expansion based on relevance feedback. In: Proceedings of the Asia-Pacific web conference, pp 233–238

  3. Bouchachia A (2009) Incremental learning. In: Encyclopedia of data warehousing and mining. IGI Global, Hershey, pp 1006–1012

  4. Bouchachia A (2011) Incremental learning with multi-level adaptation. Neurocomputing 74:1785–1799

  5. Boughanem M, Chrisment C, Tamine L (1999) Genetic approach to query space exploration. Inf Retr 1:175–192

  6. Callan J, Croft W, Harding S (1992) The inquery retrieval system. In: Proceedings of the third international conference on database and expert systems applications. Springer, Berlin, pp 78–83

  7. Cruz C, Gonzalez J, Pelta D (2011) Optimization in dynamic environments: a survey on problems, methods and measures. Soft Computing 15:1427–1448

  8. Dumais S, Furnas G, Landauer T, Deerwester S, Hrashman R (1988) Using latent semantic analysis to improve access to textual information. In: Proceedings of the conference on human factors in computing systems, pp 281–286

  9. Efron M (2008) Query expansion and dimensionality reduction: notions of optimality in rocchio relevance feedback and latent semantic indexing. Inf Process Manag 44(1):163–180

  10. Fan W, Gordon M, Pathak P (2005) Effective profiling of consumer information retrieval needs: a unified framework and empirical comparison. Decis Support Syst 40(2):213–233

  11. Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, Boston

  12. Hannani U, Shapira B, Shoval P (2001) Information filtering: overview of issues, research and systems. User Model User Adapt Interact 11(3):203–259

  13. Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments-a survey. IEEE Trans Evol Comput 9(3):303–317

  14. Kapp M, Sabourin R, Maupin P (2011) A dynamic optimization approach for adaptive incremental learning. Int J Intell Syst 26(11):1101–1124

  15. Kuflik T, Boger Z, Shoval P (2006) Filtering search results using an optimal set of terms identified by an artificial neural network. Inf Process Manage 42(2):469–483

  16. Kyamakya K, Bouchachia A, Chedjou J (eds) (2010) Intelligence for nonlinear dynamics and synchronisation. Atlantis Press, Mermaid Waters

  17. Lv Y, Zhai C (2009) Adaptive relevance feedback in information retrieval. In: CIKM, pp 255–264

  18. Nanas N, Kodovas S, Vavalis M, Houstis E (2010) Immune inspired information filtering in a high dimensional space. In: Proceedings of the 9th international conference on artificial immune systems, pp 47–60

  19. Ng H, Ang H, Soon W (1999) Dso at trec-8: a hybrid algorithm for the routing task. In: Proceedings of the fourth test retrieval conference

  20. Pickens J, Cooper M, Golovchinsky G (2010) Reverted indexing for feedback and expansion. In: CIKM, pp 1049–1058

  21. Reitter D, Lebiere C (2012) Social cognition: memory decay and adaptive information filtering for robust information maintenance. In: AAAI

  22. Ricci F, Rokach L, Shapira B, Kantor P (eds) (2011) Recommender systems handbook. Springer, Berlin

  23. Robertson S (1986) On relevance weight estimation and query expansion. J Doc 42:182–188

  24. Robertson S, Walker S, Hancock-Beaulieu M, Gutford M, Payne A (1996) Okapi at trec-4. In: Proceedings of the fourth text retrieval conference (TREC-4), pp 73–96

  25. Sahel Z, Bouchachia A, Gabrys B (2007) Adaptive mechanisms for classification problems with drifting data. In: Proceedings of the 11th international conference on knowledge-based intelligent information and engineering systems (KES’07), LNCS 4693, pp 419–426

  26. Schapire R, Singer Y, Mitra M (1998) Boosting and rocchio applied to text filtering. In: Proceedings of the ACM SIGIR’98 conference on research and development in information retrieval, Melbourne, pp 215–223

  27. Schiaffino S, Amandi A (2000) User profiling with case-based reasoning and Bayesian networks. In: International joint conference, 7th Ibero-American conference on AI, 15th Brazilian symposium on AI

  28. Schütze H, Hull D, Pedersen J (1995) A comparison of classifiers and document representations for the routing problem. In: Proceedings of the 18th annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 229–237

  29. Singhal A, Buckley C, Mitra M (1996) Pivoted document length normalization. In: Proceedings of the 19th annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 21–29

  30. Singhal A, Mitra M, Buckley C (1997) Learning routing queries in a query zone. In: Proceedings of the ACM SIGIR’97 conference on research and development in information retrieval, Philadelphia, pp 25–32

  31. Tebri H, Boughanem M, Chrisment C (2005) Incremental profile learning based on a reinforcement method. In: Proceedings of the 2005 ACM symposium on applied computing. ACM, New York, pp 1096–1101

  32. van Rijsbergen C (1979) Information retrieval. Butterwortths, London

  33. Voorhees E, Harman D (2005) TREC: experiment and evaluation in information retrieval. Digital Libraries and Electronic Publishing. MIT Press, Cambridge

  34. Woldesenbet Y, Yen G (2009) Dynamic evolutionary algorithm with variable relocation. IEEE Trans Evol Comput 13(3):500–513

  35. Xu J, Croft W (1996) Query expansion using local and global document analysis. In: Proceedings of the 19th annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 4–11

  36. Yang Y, Pedersen J (1997) A comparative study on feature selection in text categorization. In: Proceedings of the 14th international conference on machine learning. Morgan Kaufmann, Burlington, pp 412–420

  37. Yang Y, Yoo S, Zhang J, Kisiel B (2005) Robustness of adaptive filtering methods in a cross-benchmark evaluation. In Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR ’05. ACM, New York, pp 98–105

  38. Yeh J, Lin J, Ke H, Yang W (2007) Learning to rank for information retrieval using genetic programming. In Proceedings of SIGIR 2007 workshop on learning to rank for information retrieval, pp 233–238

Download references

Author information

Correspondence to Abdelhamid Bouchachia.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Bouchachia, A., Lena, A. & Vanaret, C. Online and interactive self-adaptive learning of user profile using incremental evolutionary algorithms. Evolving Systems 5, 143–157 (2014). https://doi.org/10.1007/s12530-013-9096-3

Download citation

Keywords

  • Online optimization
  • Incremental evolutionary algorithms
  • User profile learning
  • Information filtering
  • Self-adaptation
  • Self-evolving