Incremental adaptive filtering: profile learning and threshold calibration

Authors:
M. Boughanem;M. Tmar
Affiliations:
Campus Univ. Toulouse III, 118, Route de Narbonne, F-31062 Toulouse Cedex 4;Campus Univ. Toulouse III, 118, Route de Narbonne, F-31062 Toulouse Cedex 4
Venue:
Proceedings of the 2002 ACM symposium on Applied computing
Year:
2002

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Abstract

This paper proposes an adaptive filtering process. Adaptive filtering consists on receiving documents over time and compare them to the user profile. Filtering is improved over time by updating the user profile and the dissemination threshold, the profile and the threshold are the principle elements in the filtering decision function. In this paper, a linear system under constraints is resolved when a relevant document is retrieved, the solution to this system is used to improve the user profile. This allows to reinforce the relevance of each relevant retrieved document. The constraints are a form of Tf*Idf (Term frequency*Inverse document frequency). A gradient distribution approach is used, based on information extracted from relevant filtered documents to update the dissemination threshold. Experiments are undertaken into a dataset provided by TREC (Text REtrieval Conference) in order to simulate and evaluate a filtering process.