research-article

Improving Session Search by Modeling Multi-Granularity Historical Query Change

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Ji-Rong WenAuthors Info & Claims

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Pages 1534 - 1542

https://doi.org/10.1145/3488560.3498415

Published: 15 February 2022 Publication History

Abstract

In session search, it's important to utilize historical interactions between users and the search engines to improve document retrieval. However, not all historical information contributes to document ranking. Users often express their preferences in the process of modifying the previous query, which can help us catch useful information in the historical interactions. Inspired by it, we propose to model historical query change to improve document ranking performance. Especially, we characterize multi-granularity query change between each pair of adjacent queries at both term level and semantic level. For term level query change, we calculate three types of term weights, including the retained term weights, added term weights and removed term weights. Then we perform term-based interaction between the candidate document and historical queries based on the term weights. For semantic level query change, we calculate an overall representation of user intent by integrating the representations of each historical query obtained by different types of term weights. Then we adopt representation-based matching between this representation and the candidate document. To improve the effect of query change modeling, we introduce query change classification as an auxiliary task. Experimental results on AOL and TianGong-ST search logs show that our model outperforms most existing models for session search.

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Wu STu QLiu HXu JLiu ZZhang GWang RChen XYan RChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Unify Graph Learning with Text: Unleashing LLM Potentials for Session SearchProceedings of the ACM Web Conference 202410.1145/3589334.3645574(1509-1518)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645574
Liu JWang MWang JWang YChu X(2024)Long short-term search session-based document re-ranking modelKnowledge and Information Systems10.1007/s10115-024-02205-4Online publication date: 9-Sep-2024
https://doi.org/10.1007/s10115-024-02205-4
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
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cover image ACM Conferences

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

February 2022

1690 pages

ISBN:9781450391320

DOI:10.1145/3488560

General Chairs:
K. Selcuk Candan
Arizona State University, USA
,
Huan Liu
Arizona State University, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Xin Luna Dong
Meta Platforms, Inc. (former Facebook), USA
,
Jiliang Tang
Michigan State University, USA

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Published: 15 February 2022

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National Natural Science Foundation of China
China Unicom Innovation Ecological Cooperation Plan
Intelligent Social Governance Platform, Major Innovation & Planning Interdisciplinary
Platform for the Double-First Class Initiative, Renmin University of China
Beijing Outstanding Young Scientist Program

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WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining

February 21 - 25, 2022

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Cited By

Wu STu QLiu HXu JLiu ZZhang GWang RChen XYan RChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Unify Graph Learning with Text: Unleashing LLM Potentials for Session SearchProceedings of the ACM Web Conference 202410.1145/3589334.3645574(1509-1518)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645574
Liu JWang MWang JWang YChu X(2024)Long short-term search session-based document re-ranking modelKnowledge and Information Systems10.1007/s10115-024-02205-4Online publication date: 9-Sep-2024
https://doi.org/10.1007/s10115-024-02205-4
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
Wang MLiu JWang JWang YChu X(2023)A Topicality Relevance-Aware Intent Model for Web SearchIEEE Access10.1109/ACCESS.2023.328982011(65739-65748)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3289820
Wang YLiu JWang JWang XWang MChu X(2023)FE-TCM: Filter-Enhanced Transformer Click Model for Web SearchIEEE Access10.1109/ACCESS.2023.325946211(28680-28687)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3259462
Zhu YNie JSu YChen HZhang XDou ZAl Hasan MXiong L(2022)From Easy to HardProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557328(2784-2794)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557328
Chen HDou ZZhu YCao ZCheng XWen JAl Hasan MXiong L(2022)Enhancing User Behavior Sequence Modeling by Generative Tasks for Session SearchProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557310(180-190)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557310

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