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

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 6075)

Abstract

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.

Keywords

  • user model
  • user modeling
  • recommender systems
  • personalization

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References

  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)

    CrossRef  Google Scholar 

  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. 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)

    CrossRef  Google Scholar 

  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. 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. Hanani, U., Shapira, B., Shoval, P.: Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction 11, 203–259 (2001)

    CrossRef  MATH  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. 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)

    CrossRef  Google Scholar 

  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. Montaner, M., Lopez, B., De La Rossa, J.: A taxonomy of recommender agents on the internet. Artificial Intelligence Review 19, 285–330 (2003)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. Rich, E.: Users are individuals: individualizing user models. Int. Journal of Man-Machine Studies 18, 199–214 (1983)

    CrossRef  Google Scholar 

  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. 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. Schafer, J., Konstan, J., Riedl, J.: E-commerce recommender applications. Data Mining and Knowledge Discovery 5(1/2), 115–152 (2001)

    CrossRef  MATH  Google Scholar 

  20. Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)

    CrossRef  MATH  Google Scholar 

  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 

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Brun, A., Boyer, A., Razmerita, L. (2010). Compass to Locate the User Model I Need: Building the Bridge between Researchers and Practitioners in User Modeling . In: De Bra, P., Kobsa, A., Chin, D. (eds) User Modeling, Adaptation, and Personalization. UMAP 2010. Lecture Notes in Computer Science, vol 6075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13470-8_28

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  • DOI: https://doi.org/10.1007/978-3-642-13470-8_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13469-2

  • Online ISBN: 978-3-642-13470-8

  • eBook Packages: Computer ScienceComputer Science (R0)