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An Empirical Study of Embedding Features in Learning to Rank

Authors:

Faezeh Ensan,

Ebrahim Bagheri,

Amal Zouaq,

Alexandre KouznetsovAuthors Info & Claims

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Pages 2059 - 2062

https://doi.org/10.1145/3132847.3133138

Published: 06 November 2017 Publication History

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Abstract

This paper explores the possibility of using neural embedding features for enhancing the effectiveness of ad hoc document ranking based on learning to rank models. We have extensively introduced and investigated the effectiveness of features learnt based on word and document embeddings to represent both queries and documents. We employ several learning to rank methods for document ranking using embedding-based features, keyword-based features as well as the interpolation of the embedding-based features with keyword-based features. The results show that embedding features have a synergistic impact on keyword based features and are able to provide statistically significant improvement on harder queries.

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Cited By

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Aydın AArslan ADinçer B(2024)A set of novel HTML document quality features for Web information retrieval: Including applications to learning to rank for information retrievalExpert Systems with Applications10.1016/j.eswa.2024.123177246(123177)Online publication date: Jul-2024
https://doi.org/10.1016/j.eswa.2024.123177
Shehata DArabzadeh NClarke CAl Hasan MXiong L(2022)Early Stage Sparse Retrieval with Entity LinkingProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557588(4464-4469)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557588
Mahjoob MEnsan FKeshvari SJafarzadeh Pkeyvanzad M(2021)Extraction of Effective Textual and Semantic Features in Learning to Rank for Web Document RetrievalIranian Journal of Information Processing and Management10.52547/jipm.36.4.108136:4(1081-1112)Online publication date: 1-Jul-2021
https://doi.org/10.52547/jipm.36.4.1081
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Published In

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

2604 pages

ISBN:9781450349185

DOI:10.1145/3132847

General Chairs:
Ee-Peng Lim
Singapore Management University, Singapore
,
Marianne Winslett
University of Illinois at Urbana-Champaign, USA, and Advanced Digital Sciences Center, Singapore
,
Program Chairs:
Mark Sanderson
RMIT, Australia
,
Ada Fu
Chinese University of Hong Kong, Hong Kong
,
Jimeng Sun
Georgia Tech, USA
,
Shane Culpepper
RMIT, Australia
,
Eric Lo
Chinese University of Hong Kong, Hong Kong
,
Joyce Ho
Emory University, USA
,
Debora Donato
Mix Tech, Inc., USA
,
Rakesh Agrawal
Data Insights Laboratories, USA
,
Yu Zheng
Microsoft Research Asia, China
,
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Aixin Sun
Nanyang Technological University, Singapore
,
Vincent S. Tseng
National Cheng Kung University, Taiwan
,
Chenliang Li
Wuhan University, China

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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Association for Computing Machinery

New York, NY, United States

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Published: 06 November 2017

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Natural Sciences and Engineering Research Council Canada

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CIKM '17

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CIKM '17: ACM Conference on Information and Knowledge Management

November 6 - 10, 2017

Singapore, Singapore

Acceptance Rates

CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;

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

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CIKM '25

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Aydın AArslan ADinçer B(2024)A set of novel HTML document quality features for Web information retrieval: Including applications to learning to rank for information retrievalExpert Systems with Applications10.1016/j.eswa.2024.123177246(123177)Online publication date: Jul-2024
https://doi.org/10.1016/j.eswa.2024.123177
Shehata DArabzadeh NClarke CAl Hasan MXiong L(2022)Early Stage Sparse Retrieval with Entity LinkingProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557588(4464-4469)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557588
Mahjoob MEnsan FKeshvari SJafarzadeh Pkeyvanzad M(2021)Extraction of Effective Textual and Semantic Features in Learning to Rank for Web Document RetrievalIranian Journal of Information Processing and Management10.52547/jipm.36.4.108136:4(1081-1112)Online publication date: 1-Jul-2021
https://doi.org/10.52547/jipm.36.4.1081
Hosseini HNguyen TWu JBagheri EBasile VCaselli TRadicioni D(2019)Implicit entity linking in tweetsApplied Ontology10.3233/AO-19021514:4(451-477)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/AO-190215
Ensan FAl-Obeidat F(2019)Relevance-based entity selection for ad hoc retrievalInformation Processing and Management: an International Journal10.1016/j.ipm.2019.05.00556:5(1645-1666)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1016/j.ipm.2019.05.005
Lashkari FBagheri EGhorbani A(2019)Neural embedding-based indices for semantic searchInformation Processing and Management: an International Journal10.1016/j.ipm.2018.10.01556:3(733-755)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1016/j.ipm.2018.10.015
Hosseini HNguyen TBagheri E(2018)Implicit Entity Linking Through Ad-Hoc Retrieval2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM.2018.8508612(326-329)Online publication date: Aug-2018
https://doi.org/10.1109/ASONAM.2018.8508612

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ViTOR: Learning to Rank Webpages Based on Visual Features

On the usefulness of query features for learning to rank

Speeding up Document Ranking with Rank-based Features

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