skip to main content
10.1145/2187980.2188228acmotherconferencesArticle/Chapter ViewAbstractPublication PageswwwConference Proceedingsconference-collections
tutorial

Multi-application profile updates propagation: a semantic layer to improve mapping between applications

Published:16 April 2012Publication History

ABSTRACT

In the field of multi-application personalization, several techniques have been proposed to support user modeling for user data management across different applications. Many of them are based on data reconciliation techniques often implying the concepts of static ontologies and generic user data models. None of them have sufficiently investigated two main issues related to user modeling: (1) profile definition in order to allow every application to build their own view of users while promoting the sharing of these profiles and (2) profile evolution over time in order to avoid data inconsistency and the subsequent loss of income for web-site users and companies. In this paper, we conduct work and propose separated solutions for every issue. We propose a flexible user modeling system, not imposing any fixed user model whom different applications should conform to, but based on the concept of mapping among applications (and mapping functions among their user attributes). We focus in particular on the management of user profile data propagation, as a way to reduce the amount of inconsistent user profile information over several applications.

A second goal of this paper is to illustrate, in this context, the benefit obtained by the integration of a Semantic Layer that can help application designers to automatically identify potential user attribute mappings between applications.

This paper so illustrates a work-in-progress work where two complementary approaches are integrated to improve a main goal: managing multi-application user profiles in a semi-automatic manner.

References

  1. G. Adomavicius and A. Tuzhilin. Using data mining methods to build customer profiles. Computer, 34:74--82, February 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. L. Ardissono, C. Barbero, A. Goy, and G. Petrone. An agent architecture for personalized Web stores. In Proc. of AGENTS '99, pages 182--189, New York, NY, USA, 1999. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. Berkovsky, D. Heckmann, and T. Kuflik. Addressing challenges of ubiquitous user modeling: Between mediation and semantic integration. In Advances in Ubiquitous User Modelling, volume 5830 of LNCS, pages 1--19. Springer, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Berkovsky, T. Kuflik, and F. Ricci. Mediation of user models for enhanced personalization in recommender systems. User Modeling and User-Adapted Interaction, 18:245--286, 2008. 10.1007/s11257-007--9042--9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. S. Berkovsky, T. Kuflik, and F. Ricci. Cross-representation mediation of user models. User Modeling and User-Adapted Interaction, 19(1--2):35--63, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. D. Billsus and M. J. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 10(2--3):147--180, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. A. Calì, D. Calvanese, S. Colucci, T. Di Noia, and F. M. Donini. A description logic based approach for matching user profiles. In Description Logics, volume 104 of CEUR Workshop Proc., 2004.Google ScholarGoogle Scholar
  8. F. Carmagnola and F. Cena. User identification for cross-system personalisation. Journal of Information Science, 179(1--2):16--32, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. F. Carmagnola, F. Cena, O. Cortassa, C. Gena, and A. Toso. A preliminary step toward user model interoperability in the adaptive social web. In Proc. of the UbiDeUm Workshop, Corfu, Greece, 2007.Google ScholarGoogle Scholar
  10. W. Chen and R. Mizoguchi. Communication Content Ontology for Learner Model Agent in Multi-agent Architecture. In Proc. of AIED-99 workshop on Ontologies for Intelligent Educational Systems, pages 95--102, 1999.Google ScholarGoogle Scholar
  11. M. Chevalier, C. Julien, C. Soule-Dupuy, and N. Valles-Parlangeau. Personalized information access through flexible and interoperable profiles. In M. Weske, M.-S. Hacid, and C. Godart, editors, Web Information Systems Engineering, WISE 2007 Workshops, volume 4832 of Lecture Notes in Computer Science, pages 374--385. Springer Berlin / Heidelberg, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Chevalier, C. Soule-Dupuy, and P. L. Tchienehom. Profiles semantics and matching flexibility for resources access. In SITIS, pages 224--231, 2005.Google ScholarGoogle Scholar
  13. P. Dolog and M. Schäfer. A Framework for Browsing, Manipulating and Maintaining Interoperable Learner Profiles. In User Modeling, volume 3538 of LNCS, pages 397--401. Springer, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. European Telecommunication Standards Institute (ETSI). Human Factors (HF) -- User Profile Management, 2005. http://portal.etsi.org/STFs/STF_HomePages/STF342/eg_202325v010101p.pdf.Google ScholarGoogle Scholar
  15. Future of Identity in the Information Society (FIDIS). D2.3 -- Models, 2005. http://www.fidis.net/fileadmin/fidis/deliverables/fidis-wp2-del2.3.models.pdf.Google ScholarGoogle Scholar
  16. G. González, B. López, and J. Lluis de la Rosa. A Multi-agent Smart User Model for Cross-domain Recommender Systems. In Proc. of the IUI '05 Workshop on the Next Stage of Recommender Systems Research, Edinburgh, UK, 2005.Google ScholarGoogle Scholar
  17. J. E. Greer, G. I. McCalla, J. Cooke, J. A. Collins, V. Kumar, A. Bishop, and J. Vassileva. The Intelligent Helpdesk: Supporting Peer-Help in a University Course. In Proc. of ITS '98, pages 494--503, London, UK, 1998. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. D. Heckmann, T. Schwartz, B. Brandherm, and A. Kroner. Decentralized User Modeling with UserML and GUMO. In Proc. of the UM '05 DASUM Workshop, Edinburgh, UK, 2005.Google ScholarGoogle Scholar
  19. J. Kay. Ontologies for reusable and scrutable student model. In Proc. of AIED-99 workshop on Ontologies for Intelligent Educational Systems, pages 72--77, 1999.Google ScholarGoogle Scholar
  20. A. Kobsa. Generic user modeling systems. User Modeling and User-Adapted Interaction, 11(1--2):49--63, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. A. Lorenz. A Specification for Agent-Based Distributed User Modelling in Ubiquitous Computing. In Proc. of the UM '05 DASUM Workshop, Edinburgh, UK, 2005.Google ScholarGoogle Scholar
  22. B. Mehta and W. Nejdl. Intelligent Distributed User Modelling: from Semantics to Learning. In Proc. of the UbiDeUm Workshop, Edinburgh, UK, 2007.Google ScholarGoogle Scholar
  23. C. Niederee, A. Stewart, B. Mehta, and M. Hemmje. A Multi-Dimensional, Unified User Model for Cross-System Personalization. In Proc. of the AVI 2004 Workshop on Environments for Personalized Information Access, Gallipoli, Italy, 2004.Google ScholarGoogle Scholar
  24. F. Petersen, G. Bartolomeo, M. Pluke, and T. Kovacikova. An architectural framework for context sensitive personalization: standardization work at the ETSI. In Proc. of Mobility '09, pages 1--7, New York, NY, USA, 2009. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Princeton University. Wordnet -- a lexical database for english, 2010. http://wordnet.princeton.edu.Google ScholarGoogle Scholar
  26. L. Razmerita, A. A. Angehrn, and A. Maedche. Ontology-based user modeling for knowledge management systems. In User Modeling, volume 2702 of LNCS, pages 213--217. Springer, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. K. van der Sluijs and G.-J. Houben. Towards a generic user model component, 2005. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.3148.Google ScholarGoogle Scholar
  28. J. Vassileva. Distributed user modelling for universal information access. In Proc. of the 9th Int. Conf. on Human-Computer Interaction), volume 3, pages 122--126, New Orleans, USA, 2001. Lawrence Erlbaum.Google ScholarGoogle Scholar
  29. J. Vassileva, G. Mccalla, and J. Greer. Multi-agent multi-user modeling in i-help. User Modeling and User-Adapted Interaction, 13(1--2):179--210, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. M. Viviani, N. Bennani, and E. Egyed-Zsigmond. Multi-application Personalization using G-Profile. IJCSIS - Users and Information Systems, Special Issue, 2011. to appear.Google ScholarGoogle Scholar

Index Terms

  1. Multi-application profile updates propagation: a semantic layer to improve mapping between applications

              Recommendations

              Comments

              Login options

              Check if you have access through your login credentials or your institution to get full access on this article.

              Sign in
              • Published in

                cover image ACM Other conferences
                WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web
                April 2012
                1250 pages
                ISBN:9781450312301
                DOI:10.1145/2187980

                Copyright © 2012 ACM

                Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

                Publisher

                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 16 April 2012

                Permissions

                Request permissions about this article.

                Request Permissions

                Check for updates

                Qualifiers

                • tutorial

                Acceptance Rates

                Overall Acceptance Rate1,899of8,196submissions,23%

              PDF Format

              View or Download as a PDF file.

              PDF

              eReader

              View online with eReader.

              eReader