research-article

A Hybrid Framework for Session Context Modeling

Authors:

Shaoping MaAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 39, Issue 3

Article No.: 30, Pages 1 - 35

https://doi.org/10.1145/3448127

Published: 05 May 2021 Publication History

Abstract

Understanding user intent is essential for various retrieval tasks. By leveraging contextual information within sessions, e.g., query history and user click behaviors, search systems can capture user intent more accurately and thus perform better. However, most existing systems only consider intra-session contexts and may suffer from the problem of lacking contextual information, because short search sessions account for a large proportion in practical scenarios. We believe that in these scenarios, considering more contexts, e.g., cross-session dependencies, may help alleviate the problem and contribute to better performance. Therefore, we propose a novel Hybrid framework for Session Context Modeling (HSCM), which realizes session-level multi-task learning based on the self-attention mechanism. To alleviate the problem of lacking contextual information within current sessions, HSCM exploits the cross-session contexts by sampling user interactions under similar search intents in the historical sessions and further aggregating them into the local contexts. Besides, application of the self-attention mechanism rather than RNN-based frameworks in modeling session-level sequences also helps (1) better capture interactions within sessions, (2) represent the session contexts in parallelization. Experimental results on two practical search datasets show that HSCM not only outperforms strong baseline solutions such as HiNT, CARS, and BERTserini in document ranking, but also performs significantly better than most existing query suggestion methods. According to the results in an additional experiment, we have also found that HSCM is superior to most ranking models in click prediction.

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Cited By

Li ZZhang CSong DNejdl WAuer SCha MMoens MNajork M(2025)Dynamic Interaction-Driven Intent Evolver with Semantic Probability DistributionsProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703508(290-299)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703508
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
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
Show More Cited By

Index Terms

A Hybrid Framework for Session Context Modeling
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Users and interactive retrieval

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 39, Issue 3

July 2021

432 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3450607

Editor:
Min Zhang
Tsinghua University, China

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 05 May 2021

Accepted: 01 January 2021

Revised: 01 December 2020

Received: 01 September 2020

Published in TOIS Volume 39, Issue 3

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National Key Research and Development Program of China
Natural Science Foundation of China
Beijing Academy of Artificial Intelligence (BAAI)
Tsinghua University Guoqiang Research Institute, and Beijing Outstanding Young Scientist Program

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Cited By

Li ZZhang CSong DNejdl WAuer SCha MMoens MNajork M(2025)Dynamic Interaction-Driven Intent Evolver with Semantic Probability DistributionsProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703508(290-299)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703508
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
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
Liu XGuan WLi LLi HLin CLi XChen SXu JDeng HZheng BZhang ARangwala H(2022)Pretraining Representations of Multi-modal Multi-query E-commerce SearchProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539200(3429-3437)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539200

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