Abstract
The paper presents an algorithm for learning drifting and recurring user interests. The algorithm uses a prior-learning level to find out the current context. After that, searches into past observations for episodes that are relevant to the current context, ‘remembers’ them and ‘forgets’ the irrelevant ones. Finally, the algorithm learns only from the selected relevant examples. The experiments conducted with a data set about calendar scheduling recommendations show that the presented algorithm improves significantly the predictive accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Billsus, D., and Pazzani, M. J.: A Hybrid User Model for News Classification. In Kay J. (ed.), UM99: Proceedings of the Seventh International Conference on User Modeling, Lecture Notes in Computer Science, Springer-Verlag (1999) pp. 99–108.
Blum, A.: Empirical Support of Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain. Machine Learning 26 (1997): 5–23.
Brusikovsky, P. Adaptive Hypermedia. User Modeling and User-Adapted Interaction 11 (2001)87–110.
Chiu, B. and Webb, G.: Using Decision Trees for Agent Modeling: Improving Prediction Performance. User Modeling and User-Adapted Interaction 8(1/2) (1998) 131–152.
Grabtree, I. and Soltysiak, S.: Identifying and Tracking Changing Interests. International Journal of Digital Libraries vol. 2 (1998) 38–53.
Harries, M. and Sammut, C. Extracting Hidden Context. Machine Learning 32 (1998) 101–126.
Klingenberg, R. and Renz, I.: Adaptive information filtering: learning in the presence of concept drift. AAAI/ICML-98 Workshop on Learning for Text Categorization, TR WS-98-05, Madison, WI, (1998).
Kobsa, A., Koenemann, J. and Pohl, W.: Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships. The Knowledge Engineering Review, 16(2) (2001) 111–155.
Koychev, I. and Schwab, I.: Adaptation to Drifting User’s Intersects-Proceedings ECML2000/MLnet workshop: ML in the New Information Age, Barcelona, Spain, (2000) pp. 39–45.
Maloof, M. and Michalski, R.: Selecting examples for partial memory learning. Machine Learning 41 (2000) 27–52.
Mitchell, T., Caruana, R., Freitag, D., McDermott, J. and Zabowski, D.: Experience with a Learning Personal Assistant. Communications of the ACM 37(7) (1994) 81–91.
Quinlan, R.: Induction of Decision Trees. Machine Learning 1 (1986) 81–106.
Schlimmer, J. and Granger, R.: Incremental Learning from Noisy Data. Machine Learning 3, Kluwer Academic Publishers (1986), 317–357.
Webb, G. and Kuzmycz, M.: Feature-based modelling: a methodology for producing coherent, consistent, dynamically changing models of agents’ competencies. User Modeling and User-Adapted Interaction 5(2) (1996) 117–150.
Webb, G. Pazzani, M. and Billsus, D. Machine Learning for user modeling. User Modeling and User-Adaptive Interaction 11 (2001) 19–29.
Widmer, G.: Tracking Changes through Meta-Learning. Machine Learning 27 (1997) 256–286.
Widmer, G. and Kubat, M.: Learning in the presence of concept drift and hidden contexts: Machine Learning 23 (1996) 69–101.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Koychev, I. (2002). Tracking Changing User Interests through Prior-Learning of Context. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2002. Lecture Notes in Computer Science, vol 2347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47952-X_24
Download citation
DOI: https://doi.org/10.1007/3-540-47952-X_24
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43737-6
Online ISBN: 978-3-540-47952-9
eBook Packages: Springer Book Archive