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.

References

[1]

Wasi Uddin Ahmad, Kai-Wei Chang, and Hongning Wang. 2018. Multi-task learning for document ranking and query suggestion. In Proceedings of the International Conference on Learning Representations.

[2]

Wasi Uddin Ahmad, Kai-Wei Chang, and Hongning Wang. 2019. Context attentive document ranking and query suggestion. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 385–394.

Digital Library

[3]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).

[4]

Ziv Bar-Yossef and Naama Kraus. 2011. Context-sensitive query auto-completion. In Proceedings of the 20th International Conference on World Wide Web. ACM, 107–116.

Digital Library

[5]

Paul N. Bennett, Ryen W. White, Wei Chu, Susan T. Dumais, Peter Bailey, Fedor Borisyuk, and Xiaoyuan Cui. 2012. Modeling the impact of short-and long-term behavior on search personalization. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 185–194.

Digital Library

[6]

Huanhuan Cao, Daxin Jiang, Jian Pei, Enhong Chen, and Hang Li. 2009. Towards context-aware search by learning a very large variable length hidden Markov model from search logs. In Proceedings of the 18th International Conference on World Wide Web. ACM, 191–200.

Digital Library

[7]

Huanhuan Cao, Daxin Jiang, Jian Pei, Qi He, Zhen Liao, Enhong Chen, and Hang Li. 2008. Context-aware query suggestion by mining click-through and session data. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 875–883.

Digital Library

[8]

Olivier Chapelle and Ya Zhang. 2009. A dynamic Bayesian network click model for web search ranking. In Proceedings of the 18th International Conference on World Wide Web. ACM, 1–10.

Digital Library

[9]

Jia Chen, Yiqun Liu, Cheng Luo, Jiaxin Mao, Min Zhang, and Shaoping Ma. 2018. Improving session search performance with a multi-MDP model. In Proceedings of the Asia Information Retrieval Symposium. Springer, 45–59.

Digital Library

[10]

Jia Chen, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma. 2019. Investigating query reformulation behavior of search users. In Information Retrieval, Qi Zhang, Xiangwen Liao, and Zhaochun Ren (Eds.). Springer International Publishing, Cham, 39–51.

[11]

Jia Chen, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma. 2019. TianGong-ST: A new dataset with large-scale refined real-world web search sessions. In Proceedings of the 28th ACM International on Conference on Information and Knowledge Management. ACM.

Digital Library

[12]

Jia Chen, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma. 2020. A context-aware click model for web search. In Proceedings of the 13th ACM International Conference on Web Search and Data Mining. ACM.

Digital Library

[13]

Wanyu Chen, Fei Cai, Honghui Chen, and Maarten de Rijke. 2018. Attention-based hierarchical neural query suggestion. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 1093–1096.

Digital Library

[14]

Mostafa Dehghani, Sascha Rothe, Enrique Alfonseca, and Pascal Fleury. 2017. Learning to attend, copy, and generate for session-based query suggestion. In Proceedings of the ACM Conference on Information and Knowledge Management. ACM, 1747–1756.

Digital Library

[15]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

[16]

Georges E. Dupret and Benjamin Piwowarski. 2008. A user browsing model to predict search engine click data from past observations. In Proceedings of the 31st International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 331–338.

[17]

Yixing Fan, Jiafeng Guo, Yanyan Lan, Jun Xu, Chengxiang Zhai, and Xueqi Cheng. 2018. Modeling diverse relevance patterns in ad hoc retrieval. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 375–384.

Digital Library

[18]

Bruno M. Fonseca, Paulo Braz Golgher, Edleno Silva de Moura, and Nivio Ziviani. 2003. Using association rules to discover search engines related queries. In Proceedings of the IEEE/LEOS 3rd International Conference on Numerical Simulation of Semiconductor Optoelectronic Devices. IEEE, 66–71.

[19]

Wei Gao, Cheng Niu, Jian-Yun Nie, Ming Zhou, Kam-Fai Wong, and Hsiao-Wuen Hon. 2010. Exploiting query logs for cross-lingual query suggestions. ACM Trans. Inf. Syst. 28, 2 (2010), 1–33.

Digital Library

[20]

Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal. 2018. Explaining explanations: An overview of interpretability of machine learning. In Proceedings of the IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA’18). IEEE, 80–89.

[21]

Dongyi Guan, Sicong Zhang, and Hui Yang. 2013. Utilizing query change for session search. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 453–462.

Digital Library

[22]

Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft. 2016. A deep relevance matching model for ad hoc retrieval. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 55–64.

[23]

Qi He, Daxin Jiang, Zhen Liao, Steven C. H. Hoi, Kuiyu Chang, Ee-Peng Lim, and Hang Li. 2009. Web query recommendation via sequential query prediction. In Proceedings of the IEEE 25th International Conference on Data Engineering. IEEE, 1443–1454.

Digital Library

[24]

Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen. 2014. Convolutional neural network architectures for matching natural language sentences. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 2042–2050.

[25]

Chien-Kang Huang, Lee-Feng Chien, and Yen-Jen Oyang. 2003. Relevant term suggestion in interactive web search based on contextual information in query session logs. J. Amer. Soc. Inf. Sci. Technol. 54, 7 (2003), 638–649.

Digital Library

[26]

Jizhou Huang, Wei Zhang, Yaming Sun, Haifeng Wang, and Ting Liu. 2018. Improving entity recommendation with search log and multi-task learning. In Proceedings of the International Joint Conferences on Artificial Intelligence. 4107–4114.

[27]

Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. ACM, 2333–2338.

Digital Library

[28]

Jyun-Yu Jiang, Yen-Yu Ke, Pao-Yu Chien, and Pu-Jen Cheng. 2014. Learning user reformulation behavior for query auto-completion. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. 445–454.

Digital Library

[29]

Jyun-Yu Jiang and Wei Wang. 2018. RIN: Reformulation inference network for context-aware query suggestion. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 197–206.

Digital Library

[30]

Weike Jin, Zhou Zhao, Mao Gu, Jun Yu, Jun Xiao, and Yueting Zhuang. 2019. Video dialog via multi-grained convolutional self-attention context networks. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 465–474.

Digital Library

[31]

Thorsten Joachims. 1996. A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. Technical Report. Carnegie-Mellon University Department of Computer Science, Pittsburgh, PA.

[32]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

[33]

Nir Levine, Haggai Roitman, and Doron Cohen. 2017. An extended relevance model for session search. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 865–868.

Digital Library

[34]

Liangda Li, Hongbo Deng, Anlei Dong, Yi Chang, Ricardo Baeza-Yates, and Hongyuan Zha. 2017. Exploring query auto-completion and click logs for contextual-aware web search and query suggestion. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 539–548.

Digital Library

[35]

Xiaodong Liu, Jianfeng Gao, Xiaodong He, Li Deng, Kevin Duh, and Ye-Yi Wang. 2015. Representation learning using multi-task deep neural networks for semantic classification and information retrieval. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

[36]

Jiyun Luo, Sicong Zhang, and Hui Yang. 2014. Win-win search: Dual-agent stochastic game in session search. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 587–596.

Digital Library

[37]

Jiaxin Mao, Damiano Spina, Sargol Sadeghi, Falk Scholer, and Mark Sanderson. 2019. Investigating the learning process in job search: A longitudinal study. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2461–2464.

Digital Library

[38]

Kyosuke Nishida, Itsumi Saito, Atsushi Otsuka, Hisako Asano, and Junji Tomita. 2018. Retrieve-and-read: Multi-task learning of information retrieval and reading comprehension. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 647–656.

Digital Library

[39]

Rodrigo Nogueira and Kyunghyun Cho. 2017. Task-oriented query reformulation with reinforcement learning. arXiv preprint arXiv:1704.04572 (2017).

[40]

Umut Ozertem, Olivier Chapelle, Pinar Donmez, and Emre Velipasaoglu. 2012. Learning to suggest: A machine learning framework for ranking query suggestions. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. 25–34.

Digital Library

[41]

Greg Pass, Abdur Chowdhury, and Cayley Torgeson. 2006. A picture of search. In Proceedings of the 1st International Conference on Scalable Information Systems.

Digital Library

[42]

Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global vectors for word representation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’14).1532–1543.

[43]

Parikshit Ram and Alexander G. Gray. 2012. Maximum inner-product search using tree data-structures. arXiv preprint arXiv:1202.6101 (2012).

[44]

Stephen E. Robertson and Steve Walker. 1994. Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’94). Springer, 232–241.

[45]

Bahar Salehi, Fei Liu, Timothy Baldwin, and Wilson Wong. 2018. Multitask learning for query segmentation in job search. In Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval. ACM, 179–182.

Digital Library

[46]

Rodrygo L. T. Santos, Craig Macdonald, and Iadh Ounis. 2013. Learning to rank query suggestions for ad hoc and diversity search. Inf. Ret. 16, 4 (2013), 429–451.

Digital Library

[47]

Xuehua Shen, Bin Tan, and ChengXiang Zhai. 2005. Context-sensitive information retrieval using implicit feedback. In Proceedings of the 28th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 43–50.

Digital Library

[48]

Milad Shokouhi. 2013. Learning to personalize query auto-completion. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. 103–112.

Digital Library

[49]

Alessandro Sordoni, Yoshua Bengio, Hossein Vahabi, Christina Lioma, Jakob Grue Simonsen, and Jian-Yun Nie. 2015. A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 553–562.

Digital Library

[50]

Ning Su, Jiyin He, Yiqun Liu, Min Zhang, and Shaoping Ma. 2018. User intent, behaviour, and perceived satisfaction in product search. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 547–555.

Digital Library

[51]

Zhiwen Tang and Grace Hui Yang. 2019. Corpus-level end-to-end exploration for interactive systems. arXiv preprint arXiv:1912.00753 (2019).

[52]

Yuan Tian, Ke Zhou, Mounia Lalmas, Yiqun Liu, and Dan Pelleg. 2020. Cohort modeling based app usage prediction. InProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization.

Digital Library

[53]

Christophe Van Gysel, Evangelos Kanoulas, and Maarten de Rijke. 2016. Lexical query modeling in session search. In Proceedings of the ACM International Conference on the Theory of Information Retrieval. 69–72.

[54]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 5998–6008.

[55]

Bin Wu, Chenyan Xiong, Maosong Sun, and Zhiyuan Liu. 2018. Query suggestion with feedback memory network. In Proceedings of the World Wide Web Conference. International World Wide Web Conferences Steering Committee, 1563–1571.

Digital Library

[56]

Wei Wu, Hang Li, and Jun Xu. 2013. Learning query and document similarities from click-through bipartite graph with metadata. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 687–696.

Digital Library

[57]

Biao Xiang, Daxin Jiang, Jian Pei, Xiaohui Sun, Enhong Chen, and Hang Li. 2010. Context-aware ranking in web search. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 451–458.

Digital Library

[58]

Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. 2017. Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv preprint arXiv:1708.04617 (2017).

[59]

Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Maarten de Rijke, Qingyao Ai, Yufei Huang, Min Zhang, and Shaoping Ma. 2019. Improving web image search with contextual information. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1683–1692.

Digital Library

[60]

Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, and Russell Power. 2017. End-to-end neural ad hoc ranking with kernel pooling. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 55–64.

Digital Library

[61]

Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019. End-to-end open-domain question answering with BERTserini. arXiv preprint arXiv:1902.01718 (2019).

[62]

Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical attention networks for document classification. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1480–1489.

[63]

Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, and Jian Wu. 2018. Sequential recommender system based on hierarchical attention networks. In Proceedings of the 27th International Joint Conference on Artificial Intelligence.

Digital Library

[64]

Fajie Yuan, Xiangnan He, Haochuan Jiang, Guibing Guo, Jian Xiong, Zhezhao Xu, and Yilin Xiong. 2019. Future data helps training: Modeling future contexts for session-based recommendation. arXivpreprint arXiv:1906.04473 (2019).

[65]

Sicong Zhang, Dongyi Guan, and Hui Yang. 2013. Query change as relevance feedback in session search. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. 821–824.

Digital Library

[66]

Yuan Zhang, Dong Wang, and Yan Zhang. 2019. Neural IR meets graph embedding: A ranking model for product search. In Proceedings of the World Wide Web Conference. ACM, 2390–2400.

Digital Library

Cited By

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

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

Recommendations

Context Attentive Document Ranking and Query Suggestion
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

We present a context-aware neural ranking model to exploit users' on-task search activities and enhance retrieval performance. In particular, a two-level hierarchical recurrent neural network is introduced to learn search context representation of ...
A Context-Aware Click Model for Web Search
WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

To better exploit the search logs, various click models have been proposed to extract implicit relevance feedback from user clicks. Most traditional click models are based on probability graphical models (PGMs) with manually designed dependencies. ...
Context-aware query suggestion by mining click-through and session data
KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

Query suggestion plays an important role in improving the usability of search engines. Although some recently proposed methods can make meaningful query suggestions by mining query patterns from search logs, none of them are context-aware - they do not ...

Comments

Information & Contributors

Information

Published In

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

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

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].

Publisher

Association for Computing Machinery

New York, NY, United States

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

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
360
Total Downloads

Downloads (Last 12 months)36
Downloads (Last 6 weeks)3

Reflects downloads up to 27 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

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

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents