Information filtering (IF) systems usually filter data items by correlating a vector of terms that represent the user profile with similar vectors of terms that represent data items. Terms that represent data items can be determined by experts or automatic indexing methods. In this study we employ an artificial neural network (ANN) as an alternative method for both IF and term selection and compare its effectiveness to that of “traditional” methods. In an earlier study we developed and examined the performance of an IF system that employed content-based and stereotypic rule-based filtering methods in the domain of e-mail messages. In this study, we train a large-scale ANN-based filter, which uses meaningful terms in the same database as input, and use it to predict the relevance of those messages. Our results reveal that the ANN relevance prediction out-performs the prediction of the IF system. Moreover, we found very low correlation between the terms in the user profile (explicitly selected by the users) and the positive causal-index (CI) terms of the ANN, which indicate the relative importance of terms in messages. This implies that the users underestimate the importance of some terms, failing to include them in their profiles. This may explain the rather low prediction accuracy of the IF system.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.