Fuzzy Ranking Model Based on User Preference

Bo-Yeong KANG; Dae-Won KIM; Qing LI

doi:10.1093/ietisy/e89-d.6.1971

Fuzzy Ranking Model Based on User Preference

Bo-Yeong KANG, Dae-Won KIM, Qing LI

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A great deal of research has been made to model the vagueness and uncertainty in information retrieval. One such research is fuzzy ranking models, which have been showing their superior performance in handling the uncertainty involved in the retrieval process. However, these conventional fuzzy ranking models have a limited ability to incorporate the user preference when calculating the rank of documents. To address this issue, in this study we develop a new fuzzy ranking model based on the user preference. Through the experiments on the TREC-2 collection of Wall Street Journal documents, we show that the proposed method outperforms the conventional fuzzy ranking models.

Publication: IEICE TRANSACTIONS on Information Vol.E89-D No.6 pp.1971-1974

Publication Date: 2006/06/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e89-d.6.1971

Type of Manuscript: LETTER

Category: Natural Language Processing

Authors

Bo-Yeong KANG
Dae-Won KIM
Qing LI

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Bo-Yeong KANG, Dae-Won KIM, Qing LI, "Fuzzy Ranking Model Based on User Preference" in IEICE TRANSACTIONS on Information, vol. E89-D, no. 6, pp. 1971-1974, June 2006, doi: 10.1093/ietisy/e89-d.6.1971.
Abstract: A great deal of research has been made to model the vagueness and uncertainty in information retrieval. One such research is fuzzy ranking models, which have been showing their superior performance in handling the uncertainty involved in the retrieval process. However, these conventional fuzzy ranking models have a limited ability to incorporate the user preference when calculating the rank of documents. To address this issue, in this study we develop a new fuzzy ranking model based on the user preference. Through the experiments on the TREC-2 collection of Wall Street Journal documents, we show that the proposed method outperforms the conventional fuzzy ranking models.
URL: https://globals.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.6.1971/_p

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@ARTICLE{e89-d_6_1971,
author={Bo-Yeong KANG, Dae-Won KIM, Qing LI, },
journal={IEICE TRANSACTIONS on Information},
title={Fuzzy Ranking Model Based on User Preference},
year={2006},
volume={E89-D},
number={6},
pages={1971-1974},
abstract={A great deal of research has been made to model the vagueness and uncertainty in information retrieval. One such research is fuzzy ranking models, which have been showing their superior performance in handling the uncertainty involved in the retrieval process. However, these conventional fuzzy ranking models have a limited ability to incorporate the user preference when calculating the rank of documents. To address this issue, in this study we develop a new fuzzy ranking model based on the user preference. Through the experiments on the TREC-2 collection of Wall Street Journal documents, we show that the proposed method outperforms the conventional fuzzy ranking models.},
keywords={},
doi={10.1093/ietisy/e89-d.6.1971},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - Fuzzy Ranking Model Based on User Preference
T2 - IEICE TRANSACTIONS on Information
SP - 1971
EP - 1974
AU - Bo-Yeong KANG
AU - Dae-Won KIM
AU - Qing LI
PY - 2006
DO - 10.1093/ietisy/e89-d.6.1971
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2006
AB - A great deal of research has been made to model the vagueness and uncertainty in information retrieval. One such research is fuzzy ranking models, which have been showing their superior performance in handling the uncertainty involved in the retrieval process. However, these conventional fuzzy ranking models have a limited ability to incorporate the user preference when calculating the rank of documents. To address this issue, in this study we develop a new fuzzy ranking model based on the user preference. Through the experiments on the TREC-2 collection of Wall Street Journal documents, we show that the proposed method outperforms the conventional fuzzy ranking models.
ER -