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Learning Better Representations for Neural Information Retrieval with Graph Information

Authors:

Maarten de Rijke,

Shaoping MaAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 795 - 804

https://doi.org/10.1145/3340531.3411957

Published: 19 October 2020 Publication History

Abstract

Neural ranking models have recently gained much attention in Information Retrieval community and obtain good ranking performance. However, most of these retrieval models focus on capturing the textual matching signals between query and document but do not consider user behavior information that may be helpful for the retrieval task. Specifically, users' click and query reformulation behavior can be represented by a click-through bipartite graph and a session-flow graph, respectively. Such graph representations contain rich user behavior information and may help us better understand users' search intent beyond the textual information. In this study, we aim to incorporate this rich information encoded in these two graphs into existing neural ranking models.

We present two graph-based neural ranking models (\emphEmbRanker and AggRanker ) to enrich learned text representations with graph information that captures rich users' interaction behavior information. Experimental results on a large-scale publicly available benchmark dataset show that the two models outperform most existing neural ranking models that only consider textual information, which illustrates the effectiveness of integrating graph information with textual information. Further analyses show how graph information complements text matching signals and examine whether these two models can be adopted in practical applications.

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Cited By

Wang ZWei WZou DLiu YLi XMao XQiu M(2024)Exploring global information for session-based recommendationPattern Recognition10.1016/j.patcog.2023.109911145(109911)Online publication date: Jan-2024
https://doi.org/10.1016/j.patcog.2023.109911
Ma SChen CMao JTian QJiang XChen HDuh WHuang HKato MMothe JPoblete B(2023)Session Search with Pre-trained Graph Classification ModelProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591766(953-962)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591766
Albarede LMulhem PGoeuriot LMarié SLe Pape-Gardeux CChardin-Segui T(2023)Heterogeneous graph attention networks for passage retrievalInformation Retrieval10.1007/s10791-023-09424-326:1-2Online publication date: 16-Nov-2023
https://dl.acm.org/doi/10.1007/s10791-023-09424-3
Show More Cited By

Index Terms

Learning Better Representations for Neural Information Retrieval with Graph Information
1. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Content ranking

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

Copyright © 2020 ACM.

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Published: 19 October 2020

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National Key Research and Development Program of China
Natural Science Foundation of China

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CIKM '20

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CIKM '20: The 29th ACM International Conference on Information and Knowledge Management

October 19 - 23, 2020

Virtual Event, Ireland

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

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CIKM '25

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Cited By

Wang ZWei WZou DLiu YLi XMao XQiu M(2024)Exploring global information for session-based recommendationPattern Recognition10.1016/j.patcog.2023.109911145(109911)Online publication date: Jan-2024
https://doi.org/10.1016/j.patcog.2023.109911
Ma SChen CMao JTian QJiang XChen HDuh WHuang HKato MMothe JPoblete B(2023)Session Search with Pre-trained Graph Classification ModelProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591766(953-962)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591766
Albarede LMulhem PGoeuriot LMarié SLe Pape-Gardeux CChardin-Segui T(2023)Heterogeneous graph attention networks for passage retrievalInformation Retrieval10.1007/s10791-023-09424-326:1-2Online publication date: 16-Nov-2023
https://dl.acm.org/doi/10.1007/s10791-023-09424-3
Qian XRossi RDu FKim SKoh EMalik SLee TAhmed N(2022)Personalized Visualization RecommendationACM Transactions on the Web10.1145/353870316:3(1-47)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1145/3538703
Fan LLi QLiu BWu XZhang XLv FLin GLi SJin TYang K(2022)Modeling User Behavior with Graph Convolution for Personalized Product SearchProceedings of the ACM Web Conference 202210.1145/3485447.3511949(203-212)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3511949
Albarede LMulhem PGoeuriot LLe Pape-Gardeux CMarie SChardin-Segui T(2022)Passage Retrieval on Structured Documents Using Graph Attention NetworksAdvances in Information Retrieval10.1007/978-3-030-99739-7_2(13-21)Online publication date: 10-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-030-99739-7_2
Su ZDou ZZhu YQin XWen JDiaz FShah CSuel TCastells PJones RSakai T(2021)Modeling Intent Graph for Search Result DiversificationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462872(736-746)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462872

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