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A new approach to intelligent text filtering based on novelty detection

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Jean-Charles LamirelAuthors Info & Claims

ADC '06: Proceedings of the 17th Australasian Database Conference - Volume 49

Pages 149 - 156

Published: 01 January 2006 Publication History

Abstract

This paper presents an original approach to modelling user's information need in text filtering environment. This approach relies on a specific novelty detection model which allows both accurate learning of user's profile and evaluation of the coherency of user's behaviour during his interaction with the system. Thanks to an online learning algorithm, the novelty detection model is also able to track changes in user's interests over time.The proposed approach has been successfully tested on the Reuters-21578 benchmark. The experimental results prove that this approach signicantly outperforms the well-known Rocchio's learning algorithm.

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Cited By

Amplayo RHong SSong M(2018)Network-based approach to detect novelty of scholarly literatureInformation Sciences: an International Journal10.1016/j.ins.2017.09.037422:C(542-557)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.ins.2017.09.037
Kassab RLamirel JCho YWainwright RHaddad HShin SKoo Y(2007)Towards a synthetic analysis of user's information need for more effective personalized filtering servicesProceedings of the 2007 ACM symposium on Applied computing10.1145/1244002.1244190(852-859)Online publication date: 11-Mar-2007
https://dl.acm.org/doi/10.1145/1244002.1244190

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ADC '06: Proceedings of the 17th Australasian Database Conference - Volume 49

January 2006

202 pages

ISBN:1920682317

Publisher

Australian Computer Society, Inc.

Australia

Publication History

Published: 01 January 2006

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ADC '06

January 16 - 19, 2006

Hobart, Australia

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Overall Acceptance Rate 98 of 224 submissions, 44%

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Cited By

Amplayo RHong SSong M(2018)Network-based approach to detect novelty of scholarly literatureInformation Sciences: an International Journal10.1016/j.ins.2017.09.037422:C(542-557)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.ins.2017.09.037
Kassab RLamirel JCho YWainwright RHaddad HShin SKoo Y(2007)Towards a synthetic analysis of user's information need for more effective personalized filtering servicesProceedings of the 2007 ACM symposium on Applied computing10.1145/1244002.1244190(852-859)Online publication date: 11-Mar-2007
https://dl.acm.org/doi/10.1145/1244002.1244190

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