A Probabilistic Approach for Mining Drifting User Interest

Author:

Zhang Pin,Pu Juhua,Liu Yongli,Xiong Zhang

Publisher

Springer Berlin Heidelberg

Reference14 articles.

1. Koychev, I., Schwab, I.: Adaptation to Drifting User’s Interest. In: ECML Workshop: Machine Learning in New Information Age, Barcelona, Spain, pp. 39–45 (2000)

2. Billsus, D., Pazzani, M.J.: A Hybrid User Model for News Classification. In: The Seventh International Conference on User Modeling, pp. 99–108. Springer, Wien (1999)

3. Allan, J.: Incremental Relevance Feedback for Information Filtering. In: The Nineteenth International ACM-SIGIR Conference on Research and Development in Information Retrieval, pp. 270–278. ACM Press, Zurich (1996)

4. Widyantoro, D.H., Ioerger, T.R., Yen, J.: Learning User Interest Dynamics with a Three-Descriptor Representation. J. Am. Soc. Information Science 52(3), 212–225 (2001)

5. Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning 23(1), 69–101 (1996)

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