The Cubic Regression Model for Merging Results from Multiple Text Databases

Shengli Wu; Yaxin Bi; Jun Liu

doi:10.1109/SKG.2009.100

The Cubic Regression Model for Merging Results from Multiple Text Databases

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	IEEE Xplore
Pages	3-10
Number of pages	8
ISBN (Print)	978-0-7695-3810-5
DOIs	https://doi.org/10.1109/SKG.2009.100
Publication status	Published - 31 Dec 2009
Event	Semantics, Knowledge and Grid, 2009. SKG 2009. Fifth International Conference on - Zhuhai, China Duration: 31 Dec 2009 → …

Conference

Conference	Semantics, Knowledge and Grid, 2009. SKG 2009. Fifth International Conference on
Period	31/12/09 → …

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10.1109/SKG.2009.100

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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",

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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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DO - 10.1109/SKG.2009.100

M3 - Conference contribution

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Y2 - 31 December 2009

ER -

The Cubic Regression Model for Merging Results from Multiple Text Databases

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