Skip to main content
SpringerLink
Log in

P-TRIAR: Personalization Based on TRIadic Association Rules

  • Conference paper
Advances in Databases and Information Systems (ADBIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8716))

  • 1029 Accesses

Abstract

This article describes a new personalization process on decisional queries through a new approach of triadic association rules mining. This process uses the query log files of users and models them in new way by taking into account their triadic aspect. To validate our approach, we developed a personalization software prototype P-TRIAR (Personalization based on TRIadic Association Rules) which extracts two types of rules from query log files. The first one will serve to query recommendation by taking into account the collaborative aspect of users during their decisional analysis. The second type of rules will enrich user queries. The approach is tested on a real data warehouse to show the compactness of triadic association rules and the refined personalization which we propose.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)

    Article  Google Scholar 

  2. Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proc. 20th Int. Conf. Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)

    Google Scholar 

  3. Bellatreche, L., Giacometti, A., Marcel, P., Mouloudi, H., Laurent, D.: A personalization framework for olap queries. In: DOLAP, pp. 9–18 (2005)

    Google Scholar 

  4. Biedermann, K.: How triadic diagrams represent conceptual structures. In: ICCS, pp. 304–317 (1997)

    Google Scholar 

  5. Cerf, L., Besson, J., Nguyen, T.K.N., Boulicaut, J.-F.: Closed and Noise-Tolerant Patterrns in N-ary Relations. Data Mining and Knowledge Discovery 26(3), 574–619 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  6. Chatzopoulou, G., Eirinaki, M., Polyzotis, N.: Query recommendations for interactive database exploration. In: Winslett, M. (ed.) SSDBM 2009. LNCS, vol. 5566, pp. 3–18. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  7. Ganter, B., Obiedkov, S.A.: Implications in triadic formal contexts. In: ICCS, pp. 186–195 (2004)

    Google Scholar 

  8. Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer-Verlag New York, Inc. (1999), Franzke, C. (trans.)

    Google Scholar 

  9. Golfarelli, M., Rizzi, S., Biondi, P.: myolap: An approach to express and evaluate olap preferences. IEEE Trans. Knowl. Data Eng. 23(7), 1050–1064 (2011)

    Article  Google Scholar 

  10. Khemiri, R., Bentayeb, F.: Interactive query recommendation assistant. In: 2012 23rd International Workshop on Database and Expert Systems Applications (DEXA), pp. 93–97. IEEE (2012)

    Google Scholar 

  11. Khemiri, R., Bentayeb, F.: Fimioqr: Frequent itemsets mining for interactive olap query recommendation. In: DBKDA 2013, pp. 9–14 (2013)

    Google Scholar 

  12. Koutrika, G., Ioannidis, Y.: Personalized queries under a generalized preference model. In: Proceedings of 21st International Conference on Data Engineering, ICDE 2005, pp. 841–852. IEEE (2005)

    Google Scholar 

  13. Lehmann, F., Wille, R.: A triadic approach to formal concept analysis. In: ICCS, pp. 32–43 (1995)

    Google Scholar 

  14. Missaoui, R., Kwuida, L.: Mining triadic association rules from ternary relations. In: Valtchev, P., Jäschke, R. (eds.) ICFCA 2011. LNCS, vol. 6628, pp. 204–218. Springer, Heidelberg (2011)

    Google Scholar 

  15. Nguyen, T.K.N.: Generalizing Association Rules in N-ary Relations: Application to Dynamic Graph Analysis. Phd thesis, INSA de Lyon (October 2012)

    Google Scholar 

  16. Pasquier, N.: Data Mining: algorithmes d’extraction et de réduction des règles d’association dans les bases de données. PhD thesis (January 2000)

    Google Scholar 

  17. Patrick, M., Elsa, N., et al.: A survey of query recommendation techniques for datawarehouse exploration. In: EDA 2011 (2011)

    Google Scholar 

  18. Stefanidis, K., Drosou, M., Pitoura, E.: You may also like results in relational databases. In: PersDB 2009, pp. 37–42 (2009)

    Google Scholar 

  19. Trabelsi, C., Jelassi, N., Yahia, S.B.: Bgrt: une nouvelle base générique de règles d’association triadiques. application à l’autocomplétion de requêtes dans les folksonomies. Document Numérique 15(1), 101–124 (2012)

    Article  Google Scholar 

  20. Veloso, A., de Almeida, H.M., Gonçalves, M.A., Meira Jr., W.: Learning to rank at query-time using association rules. In: SIGIR, pp. 267–274 (2008)

    Google Scholar 

  21. Voutsadakis, G.: Polyadic concept analysis. Order 19(3), 295–304 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  22. Wille, R.: Restructuring lattice theory: An approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered Sets, pp. 445–470. Reidel, Dordrecht-Boston (1982)

    Chapter  Google Scholar 

  23. Wille, R.: The basic theorem of triadic concept analysis. Order 12(2), 149–158 (1995)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratoire ERIC, Université Lyon 2, Bron, France

    Selmane Sid Ali, Omar Boussaid & Fadila Bentayeb

Authors
  1. Selmane Sid Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Omar Boussaid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fadila Bentayeb
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Yannis Manolopoulos

  2. EECS Department, Northwestern University, 2145 Sheridan Road, 60208, Evanston, IL, USA

    Goce Trajcevski

  3. Faculty of Computer Sciences and Engineering, University Ss. Cyril and Methodius Skopje, Rugjer Boshkovikj 16, 1000, Skopje, Macedonia

    Margita Kon-Popovska

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Ali, S.S., Boussaid, O., Bentayeb, F. (2014). P-TRIAR: Personalization Based on TRIadic Association Rules. In: Manolopoulos, Y., Trajcevski, G., Kon-Popovska, M. (eds) Advances in Databases and Information Systems. ADBIS 2014. Lecture Notes in Computer Science, vol 8716. Springer, Cham. https://doi.org/10.1007/978-3-319-10933-6_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10933-6_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10932-9

  • Online ISBN: 978-3-319-10933-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation