Abstract
Adaptive information filtering is an emerging filtering technology that can learn the user interest/topic automatically during the filtering process and adjust its output accordingly. It provides a better performance and broader applicability than the traditional filtering technology, therefore is useful in Internet for managing sensitive information and presenting personalized content to Web user. In this paper we propose a new framework for online adaptive filtering, in which two different scoring/weighting and feedback mechanisms are implemented. Based on them, an incremental profile training method is introduced for locating user interest accurately, and a profile self-learning algorithm is also developed for adjusting user focus in test filtering. The experiments in the Reuters online news show our system performs better than the exist systems in the profile training and overall filtering results.
The research work is supported by the National 863 High Technology Project (Project No: 2001AA114040).
This is a preview of subscription content, log in via an institution to check access.
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
Baeza-ates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Chichester (1991)
Rocchio, J.: Relevance Feedback in Information Retrieval. In: Salton, Gerard (eds.) The SMART Retrieval System. Prentice-Hall, Englewood Cliffs (1971)
Schapire, R.E., Singhal, Y.S.A.: Boosting and Rocchio Applied to Text Filtering. In: 21th ACM SIGIR Conference on Research and Development in Information Retrieval (1998)
Buckley, C., et al.: The Smart/Empire TIPSTER IR system. In: Proceedings of TIPSTER Phase 3 Workshop (1999)
Roberson, S., Walker, S.: Okapi/keenbow at TREC-8. In: Proceedings of 8th Text Retrieval Conference, TREC-8 (1999)
Lafferty, J., Zhai, C.: Risk minimization and language modeling in information retrieval. In: 24th ACM SIGIR Conference on Research and Development in Information Retrieval (2001)
Zhai, C., et al.: The Lemur Toolkit for Language Modeling and Information Retrieval, http://www-2.cs.cmu.edu/~lemur/
Zhai, C., Lafferty, J.: Model-based Feedback in the Language Modeling Approach to Information Retrieval. In: The 10th International Conference on Information and Knowledge Management, CIKM (2001)
Cardie, C., Ng, V., Pierce, D., Buckley, C.: Examining the role of statistical and linguistic knowledge sources in a general knowledge question-answering system. In: Proceedings of the 6th Applied Natural Language Processing Conference (2000)
Wu, L., Huang, X., Niu, J., Guo, Y., Xia, Y., Feng, Z.: Filtering, QA, Web and Video Tasks. In: Proceeding of Text Retrieval Conference, TREC-10 (2001)
Zhai, C., Jansen, P., Roma, N., Stoica, E., Evans, D.A.: Optimization in CLARIT TREC-8 Adaptive Filtering. In: Proceeding of 8th Text Retrieval Conference, TREC-8 (1999)
Unbiased S-D Threshold Optimization, Initial Query Degradation, Incrementality, for Adaptive Filtering. In: Proceeding of 10th Text Retrieval Conference, TREC-10 (2001)
Voorhees, E.M.: Overview of the 11th Text REtrieval Conference. In: Proceeding of 11th Text Retrieval Conference, TREC-11 (2002)
Robertson, S., Soboroff, I.: The TREC 2002 Filtering Track Report. In: Proceeding of 11th Text Retrieval Conference, TREC-11 (2002)
Lin, D.: Dependency-based Evaluation of MINIPAR. In: Workshop on the Evaluation of Parsing Systems, Granada, Spain (May 1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, L., Chen, Q., Cai, L. (2003). An Improved Framework for Online Adaptive Information Filtering. In: Dong, G., Tang, C., Wang, W. (eds) Advances in Web-Age Information Management. WAIM 2003. Lecture Notes in Computer Science, vol 2762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45160-0_41
Download citation
DOI: https://doi.org/10.1007/978-3-540-45160-0_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40715-7
Online ISBN: 978-3-540-45160-0
eBook Packages: Springer Book Archive