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Elovici, Y.; Shapira, Y.B.; Kantor, P.B.: ¬A decision theoretic approach to combining information filters : an analytical and empirical evaluation. (2006) 0.05

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Abstract: The outputs of several information filtering (IF) systems can be combined to improve filtering performance. In this article the authors propose and explore a framework based on the so-called information structure (IS) model, which is frequently used in Information Economics, for combining the output of multiple IF systems according to each user's preferences (profile). The combination seeks to maximize the expected payoff to that user. The authors show analytically that the proposed framework increases users expected payoff from the combined filtering output for any user preferences. An experiment using the TREC-6 test collection confirms the theoretical findings.
Date: 22. 7.2006 15:05:39

Kantor, P.B.: Mathematical models in information science (2002) 0.02

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Source: Bulletin of the American Society for Information Science. 28(2002) no.6, S.22-24

Menkov, V.; Ginsparg, P.; Kantor, P.B.: Recommendations and privacy in the arXiv system : a simulation experiment using historical data (2020) 0.02

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Ng, K.B.; Loewenstern, D.; Basu, C.; Hirsh, H.; Kantor, P.B.: Data fusion of machine-learning methods for the TREC5 routing tak (and other work) (1997) 0.02

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Date: 27. 2.1999 20:59:22

Ng, K.B.; Kantor, P.B.; Strzalkowski, T.; Wacholder, N.; Tang, R.; Bai, B.; Rittman,; Song, P.; Sun, Y.: Automated judgment of document qualities (2006) 0.01
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Abstract

The authors report on a series of experiments to automate the assessment of document qualities such as depth and objectivity. The primary purpose is to develop a quality-sensitive functionality, orthogonal to relevance, to select documents for an interactive question-answering system. The study consisted of two stages. In the classifier construction stage, nine document qualities deemed important by information professionals were identified and classifiers were developed to predict their values. In the confirmative evaluation stage, the performance of the developed methods was checked using a different document collection. The quality prediction methods worked well in the second stage. The results strongly suggest that the best way to predict document qualities automatically is to construct classifiers on a person-by-person basis.

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