Improving document representations using relevance feedback: The RFA algorithm

Razvan Stefan Bot; Yi Fang Brook Wu

Improving document representations using relevance feedback: The RFA algorithm

Razvan Stefan Bot, Yi Fang Brook Wu

Informatics

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

5 Scopus citations

Abstract

In this paper we present a document representation improvement technique, named the Relevance Feedback Accumulation (RFA) algorithm. Using prior relevance feedback assessments and a data mining measure called "support", the algorithm's learning function gradually improves document representations, over time and across users. Results show that the modified document representations yield lower dimensionality while improving retrieval effectiveness. The algorithm is efficient and scalable, suited for retrieval systems managing large document collections.

Original language	English (US)
Title of host publication	CIKM 2004
Subtitle of host publication	Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management
Editors	D.A. Evans, L. Gravano, O. Herzog, C. Zhai, M. Ronthaler
Pages	270-278
Number of pages	9
State	Published - Dec 1 2004
Event	CIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management - Washington, DC, United States Duration: Nov 8 2004 → Nov 13 2004

Other

Other	CIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management
Country/Territory	United States
City	Washington, DC
Period	11/8/04 → 11/13/04

All Science Journal Classification (ASJC) codes

General Decision Sciences
General Business, Management and Accounting

Cite this

@inproceedings{c2a7a8ef8fa44449bf238e1ae18bb706,

title = "Improving document representations using relevance feedback: The RFA algorithm",

abstract = "In this paper we present a document representation improvement technique, named the Relevance Feedback Accumulation (RFA) algorithm. Using prior relevance feedback assessments and a data mining measure called {"}support{"}, the algorithm's learning function gradually improves document representations, over time and across users. Results show that the modified document representations yield lower dimensionality while improving retrieval effectiveness. The algorithm is efficient and scalable, suited for retrieval systems managing large document collections.",

author = "Bot, {Razvan Stefan} and Wu, {Yi Fang Brook}",

year = "2004",

month = dec,

day = "1",

language = "English (US)",

pages = "270--278",

editor = "D.A. Evans and L. Gravano and O. Herzog and C. Zhai and M. Ronthaler",

booktitle = "CIKM 2004",

note = "CIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management ; Conference date: 08-11-2004 Through 13-11-2004",

}

Bot, RS & Wu, YFB 2004, Improving document representations using relevance feedback: The RFA algorithm. in DA Evans, L Gravano, O Herzog, C Zhai & M Ronthaler (eds), CIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management. pp. 270-278, CIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management, Washington, DC, United States, 11/8/04.

Improving document representations using relevance feedback: The RFA algorithm. / Bot, Razvan Stefan; Wu, Yi Fang Brook.
CIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management. ed. / D.A. Evans; L. Gravano; O. Herzog; C. Zhai; M. Ronthaler. 2004. p. 270-278.

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

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T2 - CIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management

AU - Bot, Razvan Stefan

AU - Wu, Yi Fang Brook

PY - 2004/12/1

Y1 - 2004/12/1

N2 - In this paper we present a document representation improvement technique, named the Relevance Feedback Accumulation (RFA) algorithm. Using prior relevance feedback assessments and a data mining measure called "support", the algorithm's learning function gradually improves document representations, over time and across users. Results show that the modified document representations yield lower dimensionality while improving retrieval effectiveness. The algorithm is efficient and scalable, suited for retrieval systems managing large document collections.

AB - In this paper we present a document representation improvement technique, named the Relevance Feedback Accumulation (RFA) algorithm. Using prior relevance feedback assessments and a data mining measure called "support", the algorithm's learning function gradually improves document representations, over time and across users. Results show that the modified document representations yield lower dimensionality while improving retrieval effectiveness. The algorithm is efficient and scalable, suited for retrieval systems managing large document collections.

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M3 - Conference contribution

AN - SCOPUS:18744371590

SP - 270

EP - 278

BT - CIKM 2004

A2 - Evans, D.A.

A2 - Gravano, L.

A2 - Herzog, O.

A2 - Zhai, C.

A2 - Ronthaler, M.

Y2 - 8 November 2004 through 13 November 2004

ER -

Improving document representations using relevance feedback: The RFA algorithm

Abstract

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All Science Journal Classification (ASJC) codes

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