Compass to Locate the User Model I Need: Building the Bridge between Researchers and Practitioners in User Modeling

  • Armelle Brun
  • Anne Boyer
  • Liana Razmerita
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6075)


User modeling is a complex task, and many user modeling techniques are proposed in the existing literature, but the way these models are presented is not homogeneous, the domain is fragmented and these models are not directly comparable. Thus there is a need for a unified view of the whole user modeling domain and of the applicability of the models to specific applications, contexts or according to specific requirements, type of data, availability of data, etc. A common question companies may ask when they want to build and exploit a user model in order to implement different kinds of personalization or adaptive systems is: “Given my specific requirements, which user modeling technique can be used?”. No obvious answer can be given to this question. This article aims to propose a topic map of user modeling in connection with input data, data types, accessibility, approach, specific requirements and users’ data acquisition methods. This schema/topic map is aimed to help practitioners and researchers as well to answer the above mentioned question. Furthermore the article provides two concrete scenarios in the area of recommender systems and shows how the topic map may be used for these scenarios and real world applications.


user model user modeling recommender systems personalization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Bonnin, G., Brun, A., Boyer, A.: A low-order markov model integrating long-distance histories for collaborative recommender systems. In: Proceedings of the ACM Int. Conf. on Intelligent User Interfaces (IUI’09), Sanibel Islands, USA, February 2009, pp. 57–66 (2009)Google Scholar
  3. 3.
    Castagnos, S., Boyer, A.: Modeling preferences in a distributed recommender system. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 400–404. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Esslimani, I., Brun, A., Boyer, A.: Enhancing collaborative filtering by frequent usage patterns. In: 1st Int. Workshop on Recommender Systems and Personalized Retrieval, RSPR (2008)Google Scholar
  5. 5.
    Felfernig, A., Burke, R.: Constraint-based recommender systems: technologies and research issues. In: Fensel, D., Werthner, H. (eds.) 10th Int. Conf. on Electronic Commerce (EC’08), vol. 342 (2008)Google Scholar
  6. 6.
    Hanani, U., Shapira, B., Shoval, P.: Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction 11, 203–259 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  8. 8.
    Huang, Z., Chung, W., Chen, H.: A graph model for e-commerce recommender systems. Journal of the American Society for Information Science and Technology 55(3), 259–274 (2004)CrossRefGoogle Scholar
  9. 9.
    Lee, T., Park, Y., Park, Y.: A time-based approach to effective recommender systems using implicit feedback. Expert Systems with Applications 34(4), 3055–3062 (2008)CrossRefGoogle Scholar
  10. 10.
    Liu, K., Chen, W., Bu, J., Chen, C.: User modeling for recommendation in blogspace. In: IEEE Int. Conf. on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp. 79–82 (2007)Google Scholar
  11. 11.
    Lousame, F.P., Sanchez, E.: A taxonomy of collaborative-based recommender systems. In: Castellano, G., Jain, L., Fanelli, A. (eds.) Web Personalization in Intelligent Environments. SCI, vol. 229, pp. 81–117. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Manouselis, N., Costopoulou, C., Sideridis, A.: Introducing recommender systems for agricultural e-commerce applications. In: Int. Conf. on Inf. Systems in Sustainable Agriculture, Agroenvironment and Food Technology (2006)Google Scholar
  13. 13.
    Montaner, M., Lopez, B., De La Rossa, J.: A taxonomy of recommender agents on the internet. Artificial Intelligence Review 19, 285–330 (2003)CrossRefGoogle Scholar
  14. 14.
    Park, Y., Chang, K.: Individual and group behavior-based customer profile model for personalized product recommendation. Expert Systems with Applications 36, 1932–1939 (2009)CrossRefGoogle Scholar
  15. 15.
    Prassas, G., Pramataris, K., Papaemmanouil, O., Doukidis, G.: A recommender system for online shopping based on past customer behaviour. In: 14th Bled Electronic Commerce Conf., pp. 766–782 (2001)Google Scholar
  16. 16.
    Rich, E.: Users are individuals: individualizing user models. Int. Journal of Man-Machine Studies 18, 199–214 (1983)CrossRefGoogle Scholar
  17. 17.
    Schafer, J., Konstan, J., Ridel, J.: Recommender systems in e-commerce. In: Proceedings of 1st ACM E-Commerce Conf., pp. 158–166 (1999)Google Scholar
  18. 18.
    Yu, L., Dong, M., Wang, R.: Taxonomy for personalized recommendation service. In: Int. Symp. on Electronic Commerce and Security, pp. 657–660 (2008)Google Scholar
  19. 19.
    Schafer, J., Konstan, J., Riedl, J.: E-commerce recommender applications. Data Mining and Knowledge Discovery 5(1/2), 115–152 (2001)zbMATHCrossRefGoogle Scholar
  20. 20.
    Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)zbMATHCrossRefGoogle Scholar
  21. 21.
    Razmerita, L.: Modeling Behavior of Users in Semantic-enhanced Information Systems: The role of a User Ontology, in Adaptive Hypermedia. In: Proc. of Authoring of Adaptive and Adaptable Hypermedia Work, Hannover (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Armelle Brun
    • 1
  • Anne Boyer
    • 1
  • Liana Razmerita
    • 2
  1. 1.LORIA-Nancy UniversitéVandœuvre les Nancy
  2. 2.Copenhagen Business School, CBS, ISVFrederiksbergDenmark

Personalised recommendations