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Learning Query and Document Relevance from a Web-scale Click Graph

Authors:

Changsung Kang,

Chengxiang ZhaiAuthors Info & Claims

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

Pages 185 - 194

https://doi.org/10.1145/2911451.2911531

Published: 07 July 2016 Publication History

Abstract

Click-through logs over query-document pairs provide rich and valuable information for multiple tasks in information retrieval. This paper proposes a vector propagation algorithm on the click graph to learn vector representations for both queries and documents in the same semantic space. The proposed approach incorporates both click and content information, and the produced vector representations can directly improve ranking performance for queries and documents that have been observed in the click log. For new queries and documents that are not in the click log, we propose a two-step framework to generate the vector representation, which significantly improves the coverage of our vectors while maintaining the high quality. Experiments on Web-scale search logs from a major commercial search engine demonstrate the effectiveness and scalability of the proposed method. Evaluation results show that NDCG scores are significantly improved against multiple baselines by using the proposed method both as a ranking model and as a feature in a learning-to-rank framework.

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Cited By

Liu HGong SJi YWu KXu JGu JSerra ESpezzano F(2024)Boosting LLM-based Relevance Modeling with Distribution-Aware Robust LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680052(4718-4725)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680052
Mansour WZhuang SZuccon GMackenzie JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Revisiting Document Expansion and Filtering for Effective First-Stage RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657850(186-196)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657850
Deng BFeng LLi SQin SYang F(2024)BDP: Bipartite Graph Adversarial Defense Algorithm Based on Graph Purification2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650844(1-9)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650844
Show More Cited By

Index Terms

Learning Query and Document Relevance from a Web-scale Click Graph
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
      1. Query log analysis
    2. Retrieval models and ranking
      1. Similarity measures

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Published In

cover image ACM Conferences

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

July 2016

1296 pages

ISBN:9781450340694

DOI:10.1145/2911451

General Chairs:
Raffaele Perego
ISTI-CNR, Italy
,
Fabrizio Sebastiani
Qatar Computing Research Institute, HBKU, Qatar
,
Program Chairs:
Javed Aslam
Northeastern University, US
,
Ian Ruthven
University of Strathclyde, UK
,
Justin Zobel
University of Melbourne, Australia

Copyright © 2016 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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2016

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SIGIR '16

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SIGIR

SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval

July 17 - 21, 2016

Pisa, Italy

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SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Liu HGong SJi YWu KXu JGu JSerra ESpezzano F(2024)Boosting LLM-based Relevance Modeling with Distribution-Aware Robust LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680052(4718-4725)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680052
Mansour WZhuang SZuccon GMackenzie JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Revisiting Document Expansion and Filtering for Effective First-Stage RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657850(186-196)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657850
Deng BFeng LLi SQin SYang F(2024)BDP: Bipartite Graph Adversarial Defense Algorithm Based on Graph Purification2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650844(1-9)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650844
Su ZDou ZZhou YZhao ZWen JSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)PSLOG: Pretraining with Search Logs for Document RankingProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599477(2072-2082)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599477
Ye JZhang ZSun LYan YWang FRen F(2023)SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifoldsProceedings of the ACM Web Conference 202310.1145/3543507.3583353(360-371)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583353
Li JZeng WCheng SMa YTang JWang SYin DChen HDuh WHuang HKato MMothe JPoblete B(2023)Graph Enhanced BERT for Query UnderstandingProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591845(3315-3319)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591845
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
Gupta SOosterhuis Hde Rijke MChen HDuh WHuang HKato MMothe JPoblete B(2023)Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk MinimizationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591760(249-258)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591760
Han HXie LChen SXu H(2023)Stock trend prediction based on industry relationships driven hypergraph attention networksApplied Intelligence10.1007/s10489-023-05035-z53:23(29448-29464)Online publication date: 31-Oct-2023
https://dl.acm.org/doi/10.1007/s10489-023-05035-z
Han CCastells PGupta PXu XSalaka VAl Hasan MXiong L(2022)Addressing Cold Start in Product Search via Empirical BayesProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557066(3141-3151)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557066
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