Intelligent Information Personalization Leveraging Constraint Satisfaction and Association Rule Methods

  • Syed Sibte Raza Abidi
  • Yan Zeng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4013)


Recommender systems, using information personalization methods, provide information that is relevant to a user-model. Current information personalization methods do not take into account whether multiple documents when recommended together present a factually consistent outlook. In the realm of content-based filtering, in this paper, we investigate establishing the factual consistency between the set of documents deemed relevant to a user. We approach information personalization as a constraint satisfaction problem, where we attempt to satisfy two constraints—i.e. user-model constraints to determine the relevance of a document to a user and consistency constraints to establish factual consistency of the overall personalized information. Our information personalization framework involves: (a) an automatic constraint acquisition method, based on association rule mining, to derive consistency constraints from a corpus of documents; and (b) a hybrid of constraint satisfaction and optimization methods to derive an optimal solution comprising both relevant and factually consistent documents. We apply our information personalization framework to filter news items using the Reuters-21578 dataset.


Association Rule Information Personalization Constraint Satisfaction Constraint Satisfaction Problem Unary Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Belkin, N.J., Croft, W.B.: Information personalization and information retrieval: Two sides of the same coin? Communications of the ACM 35(12), 29–38 (1992)CrossRefGoogle Scholar
  2. 2.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)Google Scholar
  3. 3.
    Hanani, U., Shapira, B., Shoval, P.: Information Filtering: Overview of Issues, Research and Systems. User Modeling and User-Adapted Interaction 11, 203–259 (2001)MATHCrossRefGoogle Scholar
  4. 4.
    Foltz, P.W.: Using latent semantic indexing for information filtering. In: ACM SIG-OIS, pp. 40–47 (1990)Google Scholar
  5. 5.
    Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the 5th ACM Conference on Digital Libraries, San Antonio, Texas, USA, June 2000, pp. 195–204 (2000)Google Scholar
  6. 6.
    Malone, T.W., Grant, K.R., Turbak, F.A., Brobst, S.A., Cohen, M.D.: Intelligent information sharing systems. Communications of the ACM 30(5), 390–402 (1987)CrossRefGoogle Scholar
  7. 7.
    Jennings, A., Higuchi, H.: A personal news service based on a user model neural network. IEICE Transactions on Information and Systems E75-D(2), 198–210Google Scholar
  8. 8.
    Desjardins, G., Godin, R.: Combining relevance feedback and genetic algorithms in an Internet information personalization engine. In: RIAO 2000 Conference Proceedings, Paris, France, vol. 2 (2000)Google Scholar
  9. 9.
    Abidi, S.S.R., Han, C.: Constraint Satisfaction Methods for Information Personalization. In: Tawfik, A.Y., Goodwin, S.D. (eds.) Canadian AI 2004. LNCS (LNAI), vol. 3060, Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Abidi, S.S.R., Han, C.: An Adaptive Hypermedia System for Information Customization via Content Adaptation. IADIS International Journal of WWW/Internet 2(1), 79–94 (2004)Google Scholar
  11. 11.
    Han, J.W., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2000)Google Scholar
  12. 12.
    Tsang, E.: Foundations of constraint satisfaction. Academic Press, London (1993)Google Scholar
  13. 13.
    Barták, R.: Constraint programming: In pursuit of the holy grail. In: Proceedings of the Week of Doctoral Students (WDS 1999), Part IV, pp. 555–564. MatFyz Press, Prague (1999)Google Scholar
  14. 14.
    Torrens, M., Faltings, B.: SmartClients: Constraint satisfaction as a paradigm for scaleable intelligent information systems. In: Workshop on Artificial Intelligence on Electronic Commerce, AAAI-1999, Florida, USA (1999)Google Scholar
  15. 15.
    Padmanabhuni, S., You, J.H., Ghose, A.: A framework for learning constraints. In: Proc. of the PRICAI Workshop on Induction of Complex Representations (August 1996)Google Scholar
  16. 16.
    O’Sullivan, B., Freuder, E.C., O’Connell, S.: Interactive Constraint Acquisition. In: Walsh, T. (ed.) CP 2001. LNCS, vol. 2239, Springer, Heidelberg (2001)Google Scholar
  17. 17.
    Brin, S., Motwani, R., Silverstein, C.: Beyond Market Baskets - Generalizing Association Rules to Correlations. In: Proceedings of the ACM SIGMOD (1997)Google Scholar
  18. 18.
    Freuder, E., Wallace, R.: Partial Constraint Satisfaction. Artificial Intelligence 58, 21–70 (1992)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Aarts, E., Lenstra, J.K. (eds.): Local search in combinatorial optimization. Princeton University Press, Princeton (2003)MATHGoogle Scholar
  20. 20.
    Meseguer, P., Bouhmala, N., Bouzoubaa, T., Irgens, M., Sanchez, M.: Current Approaches for Solving Over-Constrained Problems. Constraints 8, 9–39 (2003)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Syed Sibte Raza Abidi
    • 1
  • Yan Zeng
    • 1
  1. 1.Faculty of Computer ScienceDalhousie UniversityHalifaxCanada

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