IRIT at TREC’2002: Filtering Track

Abstract

The experiments we undertaken this year for TREC2002 Filtering track, are focussed on threshold calibration. We proposed a new approach to calibrate the dissemination threshold in an adaptive information filtering. It consists of optimizing a utility function represented by a linearized form of the probability distributions of the scores of the relevant and the non-relevant documents already filtered. The profiles are learned using the same method used last year. It is based on a reinforcement algorithm. We submitted results on three tasks: adaptive, batch and routing. 1 Information representation and filtering Our adaptive filtering model is inspired from the connectionist model Mercure [1]. The profile and the document are represented by a set of weighted terms. The filtering process consists of computing a relevance value RSV Retrieval Status Value. The document is delivered only if the RSV is greater than the dissemination threshold. A learning process is then carried out by modifying the profile and the dissemination threshold to be more efficient in the future. 1.1 System initialization The user profile is represented by a set of terms: p (0) �