poster

A machine learning approach for improved BM25 retrieval

Authors:

Krysta M. Svore,

Christopher J.C. BurgesAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1811 - 1814

https://doi.org/10.1145/1645953.1646237

Published: 02 November 2009 Publication History

Abstract

Despite the widespread use of BM25, there have been few studies examining its effectiveness on a document description over single and multiple field combinations. We determine the effectiveness of BM25 on various document fields. We find that BM25 models relevance on popularity fields such as anchor text and query click information no better than a linear function of the field attributes. We also find query click information to be the single most important field for retrieval. In response, we develop a machine learning approach to BM25-style retrieval that learns, using LambdaRank, from the input attributes of BM25. Our model significantly improves retrieval effectiveness over BM25 and BM25F. Our data-driven approach is fast, effective, avoids the problem of parameter tuning, and can directly optimize for several common information retrieval measures. We demonstrate the advantages of our model on a very large real-world Web data collection.

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Index Terms

A machine learning approach for improved BM25 retrieval
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 November 2009

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November 2 - 6, 2009

Hong Kong, China

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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https://doi.org/10.3390/e26070579
Rungtranont PMongkolnavin J(2024)Using Large Language Models as user Interests Interpretation for Solving Cold-Start Item Recommendation2024 23rd International Symposium on Communications and Information Technologies (ISCIT)10.1109/ISCIT63075.2024.10793679(36-40)Online publication date: 23-Sep-2024
https://doi.org/10.1109/ISCIT63075.2024.10793679
Chen ZChen WXu JLiu ZZhang WFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615457(4516-4522)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615457
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