The Cubic Regression Model for Merging Results from Multiple Text Databases

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In a distributed information retrieval system, how to merge results from different text databases is an important issue, since it affects the effectiveness of the result considerably. In many cases, the underlining systems only provide a ranked list of documents for any information need. In this paper, we investigate the relation between rank and relevance in resultant document lists, and find that the cubic model is a good option for this. Extensive experimentation is conducted to evaluate the performance of the cubic model for results merging. The experimental results demonstrate that the cubic model is better than the logistic model, which was suggested by a previous research.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages3-10
Number of pages8
DOIs
Publication statusPublished - 31 Dec 2009
EventSemantics, Knowledge and Grid, 2009. SKG 2009. Fifth International Conference on - Zhuhai, China
Duration: 31 Dec 2009 → …

Conference

ConferenceSemantics, Knowledge and Grid, 2009. SKG 2009. Fifth International Conference on
Period31/12/09 → …

Fingerprint

Merging
Information retrieval systems
Logistics

Cite this

@inproceedings{89913a84d7424594a40d40123d996df3,
title = "The Cubic Regression Model for Merging Results from Multiple Text Databases",
abstract = "In a distributed information retrieval system, how to merge results from different text databases is an important issue, since it affects the effectiveness of the result considerably. In many cases, the underlining systems only provide a ranked list of documents for any information need. In this paper, we investigate the relation between rank and relevance in resultant document lists, and find that the cubic model is a good option for this. Extensive experimentation is conducted to evaluate the performance of the cubic model for results merging. The experimental results demonstrate that the cubic model is better than the logistic model, which was suggested by a previous research.",
author = "Shengli Wu and Yaxin Bi and Jun Liu",
year = "2009",
month = "12",
day = "31",
doi = "10.1109/SKG.2009.100",
language = "English",
isbn = "978-0-7695-3810-5",
pages = "3--10",
booktitle = "Unknown Host Publication",

}

Wu, S, Bi, Y & Liu, J 2009, The Cubic Regression Model for Merging Results from Multiple Text Databases. in Unknown Host Publication. pp. 3-10, Semantics, Knowledge and Grid, 2009. SKG 2009. Fifth International Conference on, 31/12/09. https://doi.org/10.1109/SKG.2009.100

The Cubic Regression Model for Merging Results from Multiple Text Databases. / Wu, Shengli; Bi, Yaxin; Liu, Jun.

Unknown Host Publication. 2009. p. 3-10.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - In a distributed information retrieval system, how to merge results from different text databases is an important issue, since it affects the effectiveness of the result considerably. In many cases, the underlining systems only provide a ranked list of documents for any information need. In this paper, we investigate the relation between rank and relevance in resultant document lists, and find that the cubic model is a good option for this. Extensive experimentation is conducted to evaluate the performance of the cubic model for results merging. The experimental results demonstrate that the cubic model is better than the logistic model, which was suggested by a previous research.

AB - In a distributed information retrieval system, how to merge results from different text databases is an important issue, since it affects the effectiveness of the result considerably. In many cases, the underlining systems only provide a ranked list of documents for any information need. In this paper, we investigate the relation between rank and relevance in resultant document lists, and find that the cubic model is a good option for this. Extensive experimentation is conducted to evaluate the performance of the cubic model for results merging. The experimental results demonstrate that the cubic model is better than the logistic model, which was suggested by a previous research.

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