Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance

Marius Köppel; Alexander Segner; Martin Wagener; Lukas Pensel; Andreas Karwath; Stefan Kramer

doi:10.1007/978-3-030-46133-1_15

Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance

Marius Köppel^*, Alexander Segner, Martin Wagener, Lukas Pensel, Andreas Karwath, Stefan Kramer

^*Corresponding author for this work

Cancer and Genomic Sciences

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

2 Citations (Scopus)

Abstract

We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that our model outperforms numerous state-of-the-art methods, while being inherently simpler in structure and using a pairwise approach only.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases
Subtitle of host publication	European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III
Editors	Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet
Publisher	Springer
Pages	237-252
Number of pages	16
Edition	1
ISBN (Electronic)	9783030461331
ISBN (Print)	9783030461324
DOIs	https://doi.org/10.1007/978-3-030-46133-1_15
Publication status	Published - 1 May 2020
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 - Wurzburg, Germany Duration: 16 Sept 2019 → 20 Sept 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11908
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
Country/Territory	Germany
City	Wurzburg
Period	16/09/19 → 20/09/19

Bibliographical note

Funding Information:
We would like to thank Dr. Christian Schmitt for his contributions to the work presented in this paper. We also thank Luiz Frederic Wagner for proof(read)ing the mathematical aspects of our model. Parts of this research were conducted using the supercomputer Mogon and/or advisory services offered by Johannes Gutenberg University Mainz (hpc.uni-mainz.de), which is a member of the AHRP (Alliance for High Performance Computing in Rhineland Palatinate, www.ahrp.info) and the Gauss Alliance e.V. The authors gratefully acknowledge the computing time granted on the supercomputer Mogon at Johannes Gutenberg University Mainz (hpc.uni-mainz.de). This research was partially funded by the Carl Zeiss Foundation Project: Competence Centre for High-Performance-Computing in the Natural Sciences? at the University of Mainz. Furthermore, Andreas Karwath has been co-funded by the MRC grant MR/S003991/1.

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

Information retrieval
Learning to rank
Machine learning

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-46133-1_15

Cite this

Köppel, M., Segner, A., Wagener, M., Pensel, L., Karwath, A., & Kramer, S. (2020). Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III (1 ed., pp. 237-252). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11908). Springer. Advance online publication. https://doi.org/10.1007/978-3-030-46133-1_15

Köppel, Marius ; Segner, Alexander ; Wagener, Martin et al. / Pairwise Learning to Rank by Neural Networks Revisited : Reconstruction, Theoretical Analysis and Practical Performance. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III. editor / Ulf Brefeld ; Elisa Fromont ; Andreas Hotho ; Arno Knobbe ; Marloes Maathuis ; Céline Robardet. 1. ed. Springer, 2020. pp. 237-252 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{a3598c5aa7b84cd598730b78bd2b559f,

title = "Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance",

abstract = "We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that our model outperforms numerous state-of-the-art methods, while being inherently simpler in structure and using a pairwise approach only.",

keywords = "Information retrieval, Learning to rank, Machine learning",

author = "Marius K{\"o}ppel and Alexander Segner and Martin Wagener and Lukas Pensel and Andreas Karwath and Stefan Kramer",

note = "Funding Information: We would like to thank Dr. Christian Schmitt for his contributions to the work presented in this paper. We also thank Luiz Frederic Wagner for proof(read)ing the mathematical aspects of our model. Parts of this research were conducted using the supercomputer Mogon and/or advisory services offered by Johannes Gutenberg University Mainz (hpc.uni-mainz.de), which is a member of the AHRP (Alliance for High Performance Computing in Rhineland Palatinate, www.ahrp.info) and the Gauss Alliance e.V. The authors gratefully acknowledge the computing time granted on the supercomputer Mogon at Johannes Gutenberg University Mainz (hpc.uni-mainz.de). This research was partially funded by the Carl Zeiss Foundation Project: Competence Centre for High-Performance-Computing in the Natural Sciences? at the University of Mainz. Furthermore, Andreas Karwath has been co-funded by the MRC grant MR/S003991/1. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 ; Conference date: 16-09-2019 Through 20-09-2019",

year = "2020",

month = may,

day = "1",

doi = "10.1007/978-3-030-46133-1_15",

language = "English",

isbn = "9783030461324",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "237--252",

editor = "Ulf Brefeld and Elisa Fromont and Andreas Hotho and Arno Knobbe and Marloes Maathuis and C{\'e}line Robardet",

booktitle = "Machine Learning and Knowledge Discovery in Databases",

edition = "1",

}

Köppel, M, Segner, A, Wagener, M, Pensel, L, Karwath, A & Kramer, S 2020, Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance. in U Brefeld, E Fromont, A Hotho, A Knobbe, M Maathuis & C Robardet (eds), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III. 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11908, Springer, pp. 237-252, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019, Wurzburg, Germany, 16/09/19. https://doi.org/10.1007/978-3-030-46133-1_15

Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance. / Köppel, Marius; Segner, Alexander; Wagener, Martin et al.
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III. ed. / Ulf Brefeld; Elisa Fromont; Andreas Hotho; Arno Knobbe; Marloes Maathuis; Céline Robardet. 1. ed. Springer, 2020. p. 237-252 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11908).

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

TY - GEN

T1 - Pairwise Learning to Rank by Neural Networks Revisited

T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019

AU - Köppel, Marius

AU - Segner, Alexander

AU - Wagener, Martin

AU - Pensel, Lukas

AU - Karwath, Andreas

AU - Kramer, Stefan

N1 - Funding Information: We would like to thank Dr. Christian Schmitt for his contributions to the work presented in this paper. We also thank Luiz Frederic Wagner for proof(read)ing the mathematical aspects of our model. Parts of this research were conducted using the supercomputer Mogon and/or advisory services offered by Johannes Gutenberg University Mainz (hpc.uni-mainz.de), which is a member of the AHRP (Alliance for High Performance Computing in Rhineland Palatinate, www.ahrp.info) and the Gauss Alliance e.V. The authors gratefully acknowledge the computing time granted on the supercomputer Mogon at Johannes Gutenberg University Mainz (hpc.uni-mainz.de). This research was partially funded by the Carl Zeiss Foundation Project: Competence Centre for High-Performance-Computing in the Natural Sciences? at the University of Mainz. Furthermore, Andreas Karwath has been co-funded by the MRC grant MR/S003991/1. Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020/5/1

Y1 - 2020/5/1

N2 - We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that our model outperforms numerous state-of-the-art methods, while being inherently simpler in structure and using a pairwise approach only.

AB - We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that our model outperforms numerous state-of-the-art methods, while being inherently simpler in structure and using a pairwise approach only.

KW - Information retrieval

KW - Learning to rank

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=85084860678&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-46133-1_15

DO - 10.1007/978-3-030-46133-1_15

M3 - Conference contribution

AN - SCOPUS:85084860678

SN - 9783030461324

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 237

EP - 252

BT - Machine Learning and Knowledge Discovery in Databases

A2 - Brefeld, Ulf

A2 - Fromont, Elisa

A2 - Hotho, Andreas

A2 - Knobbe, Arno

A2 - Maathuis, Marloes

A2 - Robardet, Céline

PB - Springer

Y2 - 16 September 2019 through 20 September 2019

ER -

Köppel M, Segner A, Wagener M, Pensel L, Karwath A, Kramer S. Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance. In Brefeld U, Fromont E, Hotho A, Knobbe A, Maathuis M, Robardet C, editors, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III. 1 ed. Springer. 2020. p. 237-252. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2020 Apr 30. doi: 10.1007/978-3-030-46133-1_15

Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this