More Web Proxy on the site http://driver.im/

research-article

Jointly Predicting Future Content in Multiple Social Media Sites Based on Multi-task Learning

Authors:

Ning GuAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 4

Article No.: 79, Pages 1 - 28

https://doi.org/10.1145/3495530

Published: 11 January 2022 Publication History

Abstract

User-generated contents (UGC) in social media are the direct expression of users’ interests, preferences, and opinions. User behavior prediction based on UGC has increasingly been investigated in recent years. Compared to learning a person’s behavioral patterns in each social media site separately, jointly predicting user behavior in multiple social media sites and complementing each other (cross-site user behavior prediction) can be more accurate. However, cross-site user behavior prediction based on UGC is a challenging task due to the difficulty of cross-site data sampling, the complexity of UGC modeling, and uncertainty of knowledge sharing among different sites. For these problems, we propose a Cross-Site Multi-Task (CSMT) learning method to jointly predict user behavior in multiple social media sites. CSMT mainly derives from the hierarchical attention network and multi-task learning. Using this method, the UGC in each social media site can obtain fine-grained representations in terms of words, topics, posts, hashtags, and time slices as well as the relevances among them, and prediction tasks in different social media sites can be jointly implemented and complement each other. By utilizing two cross-site datasets sampled from Weibo, Douban, Facebook, and Twitter, we validate our method’s superiority on several classification metrics compared with existing related methods.

References

[1]

Fabian Abel, Eelco Herder, Geert-Jan Houben, Nicola Henze, and Daniel Krause. 2013. Cross-system user modeling and personalization on the social web. User Model. User-Adapt. Interact. 23, 2-3 (2013), 169–209.

[2]

Sergey Bartunov, Anton Korshunov, Seung-Taek Park, Wonho Ryu, and Hyungdong Lee. 2012. Joint link-attribute user identity resolution in online social networks. In Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining, Workshop on Social Network Mining and Analysis.

[3]

David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3 (2003), 993–1022.

Digital Library

[4]

Yitao Cai and Xiaojun Wan. 2019. Multi-domain sentiment classification based on domain-aware embedding and attention. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. AAAI Press, 4904–4910.

[5]

Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 3 (2011), 1–27.

Digital Library

[6]

Guandan Chen, Qingchao Kong, Nan Xu, and Wenji Mao. 2019. NPP: A neural popularity prediction model for social media content. Neurocomputing 333 (2019), 221–230.

Digital Library

[7]

Terence Chen, Mohamed Ali Kaafar, Arik Friedman, and Roksana Boreli. 2012. Is more always merrier? A deep dive into online social footprints. In Proceedings of the ACM Workshop on Online Social Networks. 67–72.

Digital Library

[8]

Hyunsouk Cho, Jinyoung Yeo, and Seung-Won Hwang. 2016. Event grounding from multimodal social network fusion. In Proceedings of the IEEE 16th International Conference on Data Mining. IEEE, 835–840.

[9]

Qi Dang, Feng Gao, and Yadong Zhou. 2016. Early detection method for emerging topics based on dynamic Bayesian networks in micro-blogging networks. Expert Syst. Applic. 57 (2016), 285–295.

Digital Library

[10]

Aleksandr Farseev, Liqiang Nie, Mohammad Akbari, and Tat-Seng Chua. 2015. Harvesting multiple sources for user profile learning: A big data study. In Proceedings of the 5th ACM International Conference on Multimedia Retrieval. 235–242.

Digital Library

[11]

Ming Gao, Ee-Peng Lim, David Lo, Feida Zhu, Philips Kokoh Prasetyo, and Aoying Zhou. 2015. CNL: Collective network linkage across heterogeneous social platforms. In Proceedings of the IEEE International Conference on Data Mining. IEEE, 757–762.

Digital Library

[12]

Xiaofeng Gao, Zuowu Zheng, Quanquan Chu, Shaojie Tang, Guihai Chen, and Qianni Deng. 2021. Popularity prediction for single tweet based on heterogeneous bass model. IEEE Trans. Knowl. Data Eng. 33, 5 (2021), 2165–2178. DOI:https://doi.org/10.1109/TKDE.2019.2952856

[13]

Mahmud Hasan, Mehmet A. Orgun, and Rolf Schwitter. 2019. Real-time event detection from the Twitter data stream using the TwitterNews+ framework. Inf. Process. Manag. 56, 3 (2019), 1146–1165.

Digital Library

[14]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735–1780.

Digital Library

[15]

Haoran Huang, Qi Zhang, Jindou Wu, and Xuanjing Huang. 2017. Predicting which topics you will join in the future on social media. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 733–742.

Digital Library

[16]

Maryam Khodabakhsh, Hossein Fani, Fattane Zarrinkalam, and Ebrahim Bagheri. 2018. Predicting personal life events from streaming social content. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 1751–1754.

Digital Library

[17]

Maryam Khodabakhsh, Mohsen Kahani, and Ebrahim Bagheri. 2020. Predicting future personal life events on Twitter via recurrent neural networks. J. Intell. Inf. Syst. 54, 1 (2020), 101–127.

Digital Library

[18]

Donghyun Kim, Sungchul Kim, Handong Zhao, Sheng Li, Ryan A. Rossi, and Eunyee Koh. 2019. Domain switch-aware holistic recurrent neural network for modeling multi-domain user behavior. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. 663–671.

Digital Library

[19]

Elena Kochkina, Maria Liakata, and Arkaitz Zubiaga. 2018. All-in-one: Multi-task learning for rumour verification. In Proceedings of the 27th International Conference on Computational Linguistics. 3402–3413.

[20]

Xiangnan Kong, Jiawei Zhang, and Philip S. Yu. 2013. Inferring anchor links across multiple heterogeneous social networks. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. 179–188.

Digital Library

[21]

Jinning Li, Yirui Gao, Xiaofeng Gao, Yan Shi, and Guihai Chen. 2019. SENTI2POP: Sentiment-aware topic popularity prediction on social media. In Proceedings of the IEEE International Conference on Data Mining. IEEE, 1174–1179.

[22]

Wei Li, Shuheng Li, Shuming Ma, Yancheng He, Deli Chen, and Xu Sun. 2019. Recursive graphical neural networks for text classification. arXiv preprint arXiv:1909.08166 (2019).

[23]

Siân E. Lindley, Catherine C. Marshall, Richard Banks, Abigail Sellen, and Tim Regan. 2013. Rethinking the web as a personal archive. In Proceedings of the 22nd International Conference on World Wide Web. 749–760.

Digital Library

[24]

Pengfei Liu, Jie Fu, Yue Dong, Xipeng Qiu, and Jackie Chi Kit Cheung. 2019. Learning multi-task communication with message passing for sequence learning. In Proceedings of the AAAI Conference on Artificial Intelligence. 4360–4367.

Digital Library

[25]

Pengfei Liu, Xipeng Qiu, and Xuan-Jing Huang. 2016. Deep multi-task learning with shared memory for text classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 118–127.

[26]

Pengfei Liu, Xipeng Qiu, and Xuan-Jing Huang. 2017. Adversarial multi-task learning for text classification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 1–10.

[27]

Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019. Multi-task deep neural networks for natural language understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 4487–4496.

[28]

Xiao Ma, Xiaofeng Gao, and Guihai Chen. 2017. BEEP: A Bayesian perspective early stage event prediction model for online social networks. In Proceedings of the IEEE International Conference on Data Mining. IEEE, 973–978.

[29]

Amina Madani, Omar Boussaid, and Djamel Eddine Zegour. 2015. Real-time trending topics detection and description from Twitter content. Soc. Netw. Anal. Mining 5, 1 (2015), 59.

[30]

Anshu Malhotra, Luam Totti, Wagner Meira Jr, Ponnurangam Kumaraguru, and Virgilio Almeida. 2012. Studying user footprints in different online social networks. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 1065–1070.

Digital Library

[31]

Lydia Manikonda, Venkata Vamsikrishna, and Meduri Subbarao Kambhampati. 2016. Tweeting the mind and Instagramming the heart: Exploring differentiated content sharing on social media. In Proceedings of the 10th International Conference on Web and Social Media. AAAI Press, 639–642.

[32]

Yishu Miao, Edward Grefenstette, and Phil Blunsom. 2017. Discovering discrete latent topics with neural variational inference. In Proceedings of the 34th International Conference on Machine Learning. 2410–2419.

Digital Library

[33]

Zhongchen Miao, Kai Chen, Yi Fang, Jianhua He, Yi Zhou, Wenjun Zhang, and Hongyuan Zha. 2016. Cost-effective online trending topic detection and popularity prediction in microblogging. ACM Trans. Inf. Syst. 35, 3 (2016), 1–36.

Digital Library

[34]

Swapnil Mishra, Marian-Andrei Rizoiu, and Lexing Xie. 2016. Feature driven and point process approaches for popularity prediction. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 1069–1078.

Digital Library

[35]

Bonaventure C. Molokwu. 2019. Event prediction in complex social graphs using one-dimensional convolutional neural network. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 6450–6451.

[36]

Liqiang Nie, Xuemeng Song, and Tat-Seng Chua. 2016. Learning from multiple social networks. Synth. Lect. Inf. Concepts, Retr. Serv. 8, 2 (2016), 1–118.

[37]

Raphael Ottoni, Diego B. Las Casas, Joao Paulo Pesce, Wagner Meira Jr, Christo Wilson, Alan Mislove, and Virgílio A. F. Almeida. 2014. Of pins and tweets: Investigating how users behave across image-and text-based social networks. In Proceedings of the International Conference on Web and Social Media. AAAI Press. Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8059.

[38]

Elie Raad, Richard Chbeir, and Albert Dipanda. 2010. User profile matching in social networks. In Proceedings of the 13th International Conference on Network-based Information Systems. IEEE, 297–304.

Digital Library

[39]

Victor Sanh, Thomas Wolf, and Sebastian Ruder. 2019. A hierarchical multi-task approach for learning embeddings from semantic tasks. In Proceedings of the AAAI Conference on Artificial Intelligence. 6949–6956.

Digital Library

[40]

Manya Sleeper, William Melicher, Hana Habib, Lujo Bauer, Lorrie Faith Cranor, and Michelle L. Mazurek. 2016. Sharing personal content online: Exploring channel choice and multi-channel behaviors. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 101–112.

Digital Library

[41]

Aaron Smith and Monica Anderson. 2018. Social Media Use in 2018. Retrieved from https://www.pewinternet.org/2018/03/01/social-media-use-in-2018/.

[42]

Xuemeng Song, Zhao-Yan Ming, Liqiang Nie, Yi-Liang Zhao, and Tat-Seng Chua. 2016. Volunteerism tendency prediction via harvesting multiple social networks. ACM Trans. Inf. Syst. 34, 2 (2016), 1–27.

Digital Library

[43]

Xuemeng Song, Liqiang Nie, Luming Zhang, Mohammad Akbari, and Tat-Seng Chua. 2015. Multiple social network learning and its application in volunteerism tendency prediction. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 213–222.

Digital Library

[44]

Xuemeng Song, Liqiang Nie, Luming Zhang, Maofu Liu, and Tat-Seng Chua. 2015. Interest inference via structure-constrained multi-source multi-task learning. In Proceedings of the 24th International Conference on Artificial Intelligence. 2371–2377.

Digital Library

[45]

Alexandru Tatar, Marcelo Dias De Amorim, Serge Fdida, and Panayotis Antoniadis. 2014. A survey on predicting the popularity of web content. J. Internet Serv. Applic. 5, 1 (2014), 8.

[46]

Bing Tian, Yong Zhang, Jin Wang, and Chunxiao Xing. 2019. Hierarchical inter-attention network for document classification with multi-task learning. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. AAAI Press, 3569–3575.

Digital Library

[47]

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 Conference on Advances in Neural Information Processing Systems. 5998–6008.

Digital Library

[48]

Xiang Wang, Chen Wang, Zhaoyun Ding, Min Zhu, and Jiumin Huang. 2018. Predicting the popularity of topics based on user sentiment in microblogging websites. J. Intell. Inf. Syst. 51, 1 (2018), 97–114.

Digital Library

[49]

Fangzhao Wu and Yongfeng Huang. 2016. Sentiment domain adaptation with multiple sources. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 301–310.

[50]

Fangzhao Wu, Zhigang Yuan, and Yongfeng Huang. 2017. Collaboratively training sentiment classifiers for multiple domains. IEEE Trans. Knowl. Data Eng. 29, 7 (2017), 1370–1383.

Digital Library

[51]

Lianwei Wu, Yuan Rao, Haolin Jin, Ambreen Nazir, and Ling Sun. 2019. Different absorption from the same sharing: Sifted multi-task learning for fake news detection. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 4636–4645.

[52]

Yuan Wu and Yuhong Guo. 2020. Dual adversarial co-learning for multi-domain text classification. In Proceedings of the 34th AAAI Conference on Artificial Intelligence. AAAI Press, 6438–6445.

[53]

Tong Xu, Hengshu Zhu, Hao Zhong, Guannan Liu, Hui Xiong, and Enhong Chen. 2018. Exploiting the dynamic mutual influence for predicting social event participation. IEEE Trans. Knowl. Data Eng. 31, 6 (2018), 1122–1135.

[54]

Pengcheng Yang, Xu Sun, Wei Li, Shuming Ma, Wei Wu, and Houfeng Wang. 2018. SGM: Sequence generation model for multi-label classification. In Proceedings of the 27th International Conference on Computational Linguistics. 3915–3926.

[55]

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.

[56]

Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu. 2014. Social Media Mining: An Introduction. Cambridge University Press.

[57]

Reza Zafarani and Huan Liu. 2013. Connecting users across social media sites: A behavioral-modeling approach. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 41–49.

Digital Library

[58]

Reza Zafarani, Lei Tang, and Huan Liu. 2015. User identification across social media. ACM Trans. Knowl. Discov. Data 10, 2 (2015), 1–30.

Digital Library

[59]

Peng Zhang, Haiyi Zhu, Tun Lu, Hansu Gu, Wenjian Huang, and Ning Gu. 2017. Understanding relationship overlapping on social network sites: A case study of Weibo and Douban. Proc. ACM Hum.-comput. Interact. 1, CSCW (2017), 1–18.

Digital Library

[60]

Shuo Zhang and Qin Lv. 2018. Hybrid EGU-based group event participation prediction in event-based social networks. Knowl.-based Syst. 143 (2018), 19–29.

[61]

Wei Zhang, Wen Wang, Jun Wang, and Hongyuan Zha. 2018. User-guided hierarchical attention network for multi-modal social image popularity prediction. In Proceedings of the World Wide Web Conference. 1277–1286.

Digital Library

[62]

Juanjuan Zhao, Weili Wu, Xiaolong Zhang, Yan Qiang, Tao Liu, and Lidong Wu. 2013. A short-term prediction model of topic popularity on microblogs. In Proceedings of the International Computing and Combinatorics Conference. Springer, 759–769.

[63]

Xuan Zhao, Cliff Lampe, and Nicole B. Ellison. 2016. The social media ecology: User perceptions, strategies and challenges. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 89–100.

Digital Library

[64]

Xuan Zhao and Siân E. Lindley. 2014. Curation through use: Understanding the personal value of social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2431–2440.

Digital Library

[65]

Changtao Zhong, Hau-wen Chan, Dmytro Karamshuk, Dongwon Lee, and Nishanth Sastry. 2017. Wearing many (social) hats: How different are your different social network personae? In Proceedings of the 11th International Conference on Web and Social Media. AAAI Press, 397–407.

[66]

Xiaoping Zhou, Xun Liang, Haiyan Zhang, and Yuefeng Ma. 2015. Cross-platform identification of anonymous identical users in multiple social media networks. IEEE Trans. Knowl. Data Eng. 28, 2 (2015), 411–424.

Digital Library

Cited By

Yang Y(2024)Research on Innovative Practice and Theoretical Exploration of Visual Communication Design for Future MediaApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-16699:1Online publication date: 5-Jul-2024
https://doi.org/10.2478/amns-2024-1669
Zhou QZhang PGu HLu TGu N(2024)Exploring Cross-Site User Modeling without Cross-Site User Identity Linkage: A Case Study of Content Preference PredictionACM Transactions on Information Systems10.1145/369783243:1(1-28)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1145/3697832
Ekle OEberle W(2024)Anomaly Detection in Dynamic Graphs: A Comprehensive SurveyACM Transactions on Knowledge Discovery from Data10.1145/3669906Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3669906
Show More Cited By

Index Terms

Jointly Predicting Future Content in Multiple Social Media Sites Based on Multi-task Learning
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
      1. Social networking sites

Recommendations

Uses and gratifications of social networking sites for bridging and bonding social capital

Applying uses and gratifications theory (UGT) and social capital theory, our study examined users of four social networking sites (SNSs) (Facebook, Twitter, Instagram, and Snapchat), and their influence on online bridging and bonding social capital. ...
One Social Movement, Two Social Media Sites: A Comparative Study of Public Discourses

Social media have become central places where public discourses are generated, sustained, and circulated around public events. So far, much research has examined large-scale dissemination patterns of prominent statements, opinions, and slogans ...
Identifying content for planned events across social media sites
WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining

User-contributed Web data contains rich and diverse information about a variety of events in the physical world, such as shows, festivals, conferences and more. This information ranges from known event features (e.g., title, time, location) posted on ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 4

October 2022

812 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3501285

Editor:
Min Zhang
Tsinghua University, China

Issue’s Table of Contents

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: 11 January 2022

Accepted: 01 November 2021

Revised: 01 June 2021

Received: 01 January 2021

Published in TOIS Volume 40, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

National Natural Science Foundation of China (NSFC)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
943
Total Downloads

Downloads (Last 12 months)182
Downloads (Last 6 weeks)17

Reflects downloads up to 10 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yang Y(2024)Research on Innovative Practice and Theoretical Exploration of Visual Communication Design for Future MediaApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-16699:1Online publication date: 5-Jul-2024
https://doi.org/10.2478/amns-2024-1669
Zhou QZhang PGu HLu TGu N(2024)Exploring Cross-Site User Modeling without Cross-Site User Identity Linkage: A Case Study of Content Preference PredictionACM Transactions on Information Systems10.1145/369783243:1(1-28)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1145/3697832
Ekle OEberle W(2024)Anomaly Detection in Dynamic Graphs: A Comprehensive SurveyACM Transactions on Knowledge Discovery from Data10.1145/3669906Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3669906
Li YWang SXu SYin J(2024)Trustworthy semi‐supervised anomaly detection for online‐to‐offline logistics business in merchant identificationCAAI Transactions on Intelligence Technology10.1049/cit2.123019:3(544-556)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1049/cit2.12301
Wang YChen JLi QHe CGao J(2024)Graph neural architecture search with heterogeneous message-passing mechanismsKnowledge and Information Systems10.1007/s10115-024-02090-x66:7(4283-4308)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1007/s10115-024-02090-x
Xu MFu YTian B(2023)An ensemble fraud detection approach for online loans based on application usage patternsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22240544:5(7181-7194)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-222405
Li HZhang ZWang XZhu W(2023)Invariant Node Representation Learning under Distribution Shifts with Multiple Latent EnvironmentsACM Transactions on Information Systems10.1145/360442742:1(1-30)Online publication date: 18-Aug-2023
https://dl.acm.org/doi/10.1145/3604427
Somanchi SAbbasi AKelley KDobolyi DYuan T(2023)Examining User Heterogeneity in Digital ExperimentsACM Transactions on Information Systems10.1145/357893141:4(1-34)Online publication date: 22-Mar-2023
https://dl.acm.org/doi/10.1145/3578931
Feng WLiu SCheng X(2023)Hierarchical Dense Pattern Detection in TensorsACM Transactions on Knowledge Discovery from Data10.1145/357702217:6(1-29)Online publication date: 28-Feb-2023
https://dl.acm.org/doi/10.1145/3577022
Wei XGong XZhan YDu BLuo YHu WChua TLauw HSi LTerzi ETsaparas P(2023)CLNode: Curriculum Learning for Node ClassificationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570385(670-678)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570385
Show More Cited By

View Options

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.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents