Abstract
We present the exploitation of an improved version of the Learning Server for modeling the user interaction in a digital library service architecture. This module is the basic component for providing the service with an added value such as an essential extensible form of interface adaptivity. Indeed, the system is equipped with a web-based visual environment, primarily intended to improve the user interaction by automating the assignment of a suitable interface depending on data relative to the previous experience with the system, coded in log files. The experiments performed show that accurate interaction models can be inferred automatically by using up-to-date learning algorithms.
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References
Banyon, D., and Murray, D.: Applying User Modeling to Human-Computer Interaction Design. Artificial Intelligence Review, 7 (1993) 199–225.
Bernstein, P.A.: Middleware: A Model for Distributed System Services, Communications of the ACM, 39(2) (1996) 86–98.
Billsus, D.and Pazzani, M.: Learning probabilistic user models. In: Proceedings of the 6th International Conference on User Modelling, Chia Laguna, Italy, 1996.
Costabile, M.F., Esposito, F. Semeraro, G. and Fanizzi, N.: An Adaptive Visual Environment for Digital Libraries. In International Journal of Digital Libraries, 2(2+3), Springer (1999) 124–143.
Esposito, F., Malerba, D., Semeraro, G., Fanizzi, N., and Ferilli, S.: Adding Machine Learning and Knowledge Intensive Techniques to a Digital Library Service. International Journal of Digital Libraries 2(1), Springer (1998) 3–19.
Moustakis, V.S., and Herrmann, J.: Where Do Machine Learning and Human-Computer Interaction Meet?. Applied Artificial Intelligence, 11 (1997) 595–609.
McChesney, M.C.: Banking in Cyberspace: An Investment in Itself. IEEE Spectrum, February (1997) 54–59.
Quinlan, J.R.: Improved Use of Continuous Attributes in C4.5, Journal of Artificial Intelligence Research, 4 (1996) 77–90.
Rao, R., Pedersen, J.O., Hearst, M.A., Mackinlay, J.D., Card, S.K., Masinster, L., Halvorsen, P.-K., and Robertson, G.G.: Rich interaction in the digital library. Communications of the ACM, 38(4) (1995) 29–39
Salton, G., and McGill, M.J.: Introduction to Modern Information Retrieval, New York, NY, McGraw-Hill (1983).
Semeraro, G., Esposito, F., Malerba, D., Fanizzi, N., and Ferilli, S.: Machine Learning + On-line Libraries = IDL. In: C. Peters C. and Thanos C. (eds.), Proceedings of the first European Conference on Research and Advanced Technologies for Digital Libraries, ECDL’97, Pisa, Italy, Sept. 1997. LNCS 1324. Springer (1997) 195–214.
Semeraro, G., Costabile, M.F., Esposito, F., Fanizzi, N., and Ferilli, S.: A Learning Server for Inducing User Classification Rules in a Digital Library Service. In Z.W. Ras and A. Skowron (Eds.), Foundations of Intelligent Systems, LNAI 1609, Springer, (1999) 208–216.
COGITO Project IST-1999-13347 website: http://www.gmd.darmstadt.de/~cogito.
Witten Ian H. and Frank E: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco CA (2000).
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Semeraro, G., Ferilli, S., Fanizzi, N., Abbattista, F. (2001). Learning Interaction Models in a Digital Library Service. In: Bauer, M., Gmytrasiewicz, P.J., Vassileva, J. (eds) User Modeling 2001. UM 2001. Lecture Notes in Computer Science(), vol 2109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44566-8_5
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DOI: https://doi.org/10.1007/3-540-44566-8_5
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