research-article

Sequential Recommendation via an Adaptive Cross-domain Knowledge Decomposition

Authors:

Jianping FanAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 3453 - 3463

https://doi.org/10.1145/3583780.3615058

Published: 21 October 2023 Publication History

Abstract

Cross-domain recommendation, as an intelligent machine to alleviate data sparsity and cold start problems, has attracted extensive attention from scholars. Existing cross-domain recommendation frameworks usually leverage overlapping entities for knowledge transfer, the most popular of which are information aggregation and consistency maintenance. Despite decent improvements, the neglect of dynamic perspectives, the presence of confounding factors, and the disparities in domain properties inevitably constrain model performance. In view of this, this paper proposes a sequential recommendation framework via adaptive cross-domain knowledge decomposition, namely ARISEN, which focuses on employing adaptive causal learning to improve recommendation performance. Specifically, in order to facilitate sequence transfer, we align the user's behaviour sequences in the source domain and target domain according to the timestamps, expecting to use the abundant semantics of the former to augment the information of the latter. Regarding confounding factor removal, we introduce the causal learning technique and promote it as an adaptive representation decomposition framework on the basis of instrumental variables. For the sake of alleviating the impact of domain disparities, this paper endeavors to employ two mutually orthogonal transformation matrices for information fusion. Extensive experiments and detailed analyzes on large industrial and public data sets demonstrate that our framework can achieve substantial improvements over state-of-the-art algorithms.

References

[1]

Ali Abdallah Alalwan. 2018. Investigating the impact of social media advertising features on customer purchase intention. International Journal of Information Management, Vol. 42 (2018), 65--77.

[2]

Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf. 2021. Variable-lag granger causality and transfer entropy for time series analysis. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 15, 4 (2021), 1--30.

[3]

Joshua D Angrist and Jörn-Steffen Pischke. 2009. Mostly harmless econometrics: An empiricist's companion. Princeton university press.

[4]

Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. 2018. Mutual information neural estimation. In International conference on machine learning. PMLR, 531--540.

[5]

Andrew Bennett, Nathan Kallus, and Tobias Schnabel. 2019. Deep generalized method of moments for instrumental variable analysis. Advances in neural information processing systems, Vol. 32 (2019).

[6]

Mehmet Caner and Bruce E Hansen. 2004. Instrumental variable estimation of a threshold model. Econometric Theory, Vol. 20, 5 (2004), 813--843.

[7]

Jiangxia Cao, Jiawei Sheng, Xin Cong, Tingwen Liu, and Bin Wang. 2022. Cross-domain recommendation to cold-start users via variational information bottleneck. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2209--2223.

[8]

Chaochao Chen, Huiwen Wu, Jiajie Su, Lingjuan Lyu, Xiaolin Zheng, and Li Wang. 2022b. Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation. In Proceedings of the ACM Web Conference 2022. 1455--1465.

Digital Library

[9]

Chong Chen, Min Zhang, Chenyang Wang, Weizhi Ma, Minming Li, Yiqun Liu, and Shaoping Ma. 2019. An efficient adaptive transfer neural network for social-aware recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 225--234.

Digital Library

[10]

Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2020. Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems (2020).

[11]

Zhihong Chen, Jiawei Wu, Chenliang Li, Jingxu Chen, Rong Xiao, and Binqiang Zhao. 2022a. Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 60--69.

Digital Library

[12]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. ArXiv, Vol. abs/1810.04805 (2019).

[13]

Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. 2015. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the 24th international conference on world wide web. 278--288.

Digital Library

[14]

Wenjing Fu, Zhaohui Peng, Senzhang Wang, Yang Xu, and Jin Li. 2019. Deeply fusing reviews and contents for cold start users in cross-domain recommendation systems. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 94--101.

Digital Library

[15]

Lei Guo, Li Tang, Tong Chen, Lei Zhu, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2021. DA-GCN: a domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. arXiv preprint arXiv:2105.03300 (2021).

[16]

Lars Peter Hansen. 1982. Large sample properties of generalized method of moments estimators. Econometrica: Journal of the econometric society (1982), 1029--1054.

[17]

Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. 2017. Deep IV: A flexible approach for counterfactual prediction. In International Conference on Machine Learning. PMLR, 1414--1423.

[18]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173--182.

Digital Library

[19]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).

[20]

Guangneng Hu, Yu Zhang, and Qiang Yang. 2018a. Conet: Collaborative cross networks for cross-domain recommendation. In Proceedings of the 27th ACM international conference on information and knowledge management. 667--676.

Digital Library

[21]

Guangneng Hu, Yu Zhang, and Qiang Yang. 2018b. MTNet: a neural approach for cross-domain recommendation with unstructured text. KDD Deep Learning Day (2018), 1--10.

[22]

Thorsten Joachims, Adith Swaminathan, and Tobias Schnabel. 2017. Unbiased learning-to-rank with biased feedback. In Proceedings of the tenth ACM international conference on web search and data mining. 781--789.

Digital Library

[23]

SeongKu Kang, Junyoung Hwang, Dongha Lee, and Hwanjo Yu. 2019. Semi-supervised learning for cross-domain recommendation to cold-start users. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1563--1572.

Digital Library

[24]

Adit Krishnan, Mahashweta Das, Mangesh Bendre, Hao Yang, and Hari Sundaram. 2020. Transfer learning via contextual invariants for one-to-many cross-domain recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1081--1090.

Digital Library

[25]

Pan Li and Alexander Tuzhilin. 2020. Ddtcdr: Deep dual transfer cross domain recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining. 331--339.

Digital Library

[26]

Pan Li and Alexander Tuzhilin. 2021. Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations. IEEE Transactions on Knowledge and Data Engineering, Vol. 35 (2021), 321--334. https://api.semanticscholar.org/CorpusID:233296239

[27]

Siqing Li, Liuyi Yao, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Tonglei Guo, Bolin Ding, and Ji-Rong Wen. 2021. Debiasing Learning based Cross-domain Recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3190--3199.

Digital Library

[28]

Zohreh Ovaisi, Ragib Ahsan, Yifan Zhang, Kathryn Vasilaky, and Elena Zheleva. 2020. Correcting for selection bias in learning-to-rank systems. In Proceedings of The Web Conference 2020. 1863--1873.

Digital Library

[29]

Yu Peng. 2022. A Survey on Modern Recommendation System based on Big Data. ArXiv, Vol. abs/2206.02631 (2022).

[30]

Gerard Prendergast, David Ko, and V Yuen Siu Yin. 2010. Online word of mouth and consumer purchase intentions. International journal of advertising, Vol. 29, 5 (2010), 687--708.

[31]

Muhammad Usman Riaz, Luo Xiao Guang, Maria Zafar, Fakhar Shahzad, Muhammad Shahbaz, and Majid Lateef. 2021. Consumers' purchase intention and decision-making process through social networking sites: a social commerce construct. Behaviour & Information Technology, Vol. 40, 1 (2021), 99--115.

[32]

Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In international conference on machine learning. PMLR, 1670--1679.

[33]

Zihua Si, Xueran Han, Xiao Zhang, Jun Xu, Yue Yin, Yang Song, and Ji-Rong Wen. 2022. A Model-Agnostic Causal Learning Framework for Recommendation using Search Data. In Proceedings of the ACM Web Conference 2022. 224--233.

Digital Library

[34]

Ajit P Singh and Geoffrey J Gordon. 2008. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 650--658.

Digital Library

[35]

Rahul Singh, Maneesh Sahani, and Arthur Gretton. 2019. Kernel Instrumental Variable Regression. In Neural Information Processing Systems.

[36]

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, and Zhiwei Steven Wu. 2022. Causal Imitation Learning under Temporally Correlated Noise. In International Conference on Machine Learning.

[37]

Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the eleventh ACM international conference on web search and data mining. 565--573.

Digital Library

[38]

Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).

[39]

Shoujin Wang, Longbing Cao, Yan Wang, Quan Z Sheng, Mehmet A Orgun, and Defu Lian. 2021. A survey on session-based recommender systems. ACM Computing Surveys (CSUR), Vol. 54, 7 (2021), 1--38.

Digital Library

[40]

Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z Sheng, and Mehmet Orgun. 2019. Sequential recommender systems: challenges, progress and prospects. arXiv preprint arXiv:2001.04830 (2019).

[41]

Yixin Wang, Dawen Liang, Laurent Charlin, and David M Blei. 2020. Causal inference for recommender systems. In Proceedings of the 14th ACM Conference on Recommender Systems. 426--431.

Digital Library

[42]

Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, and Xiangnan He. 2021. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1791--1800.

Digital Library

[43]

Liwei Wu, Shuqing Li, Cho-Jui Hsieh, and James Sharpnack. 2020. SSE-PT: Sequential recommendation via personalized transformer. In Fourteenth ACM Conference on Recommender Systems. 328--337.

Digital Library

[44]

Ruobing Xie, Qi Liu, Liangdong Wang, Shukai Liu, Bo Zhang, and Leyu Lin. 2022. Contrastive cross-domain recommendation in matching. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4226--4236.

Digital Library

[45]

Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang. 2021. A survey on causal inference. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 15, 5 (2021), 1--46.

[46]

Junkun Yuan, Anpeng Wu, Kun Kuang, Bo Li, Runze Wu, Fei Wu, and Lanfen Lin. 2022. Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 16, 4 (2022), 1--20.

[47]

Tianzi Zang, Yanmin Zhu, Haobing Liu, Ruohan Zhang, and Jiadi Yu. 2022. A survey on cross-domain recommendation: taxonomies, methods, and future directions. ACM Transactions on Information Systems, Vol. 41, 2 (2022), 1--39.

Digital Library

[48]

Qian Zhang, Jie Lu, Dianshuang Wu, and Guangquan Zhang. 2018. A cross-domain recommender system with kernel-induced knowledge transfer for overlapping entities. IEEE transactions on neural networks and learning systems, Vol. 30, 7 (2018), 1998--2012.

[49]

Qian Zhang, Dianshuang Wu, Jie Lu, Feng Liu, and Guangquan Zhang. 2017. A cross-domain recommender system with consistent information transfer. Decision Support Systems, Vol. 104 (2017), 49--63.

Digital Library

[50]

Xiaokun Zhang, Hongfei Lin, Bo Xu, Chenliang Li, Yuan Lin, Haifeng Liu, and Fenglong Ma. 2022. Dynamic intent-aware iterative denoising network for session-based recommendation. Information Processing & Management, Vol. 59, 3 (2022), 102936.

Digital Library

[51]

Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, and Aixin Sun. 2020. CATN: Cross-domain recommendation for cold-start users via aspect transfer network. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 229--238.

Digital Library

[52]

Chuang Zhao, Hongke Zhao, Ming HE, Jian Zhang, and Jianping Fan. 2023 a. Cross-Domain Recommendation via User Interest Alignment. In Proceedings of the ACM Web Conference 2023 (Austin, TX, USA) (WWW '23). Association for Computing Machinery, New York, NY, USA, 887--896. https://doi.org/10.1145/3543507.3583263

Digital Library

[53]

Chuang Zhao, Hongke Zhao, Runze Wu, Qilin Deng, Yu Ding, Jianrong Tao, and Changjie Fan. 2022. Multi-Dimensional Prediction of Guild Health in Online Games: A Stability-Aware Multi-Task Learning Approach. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 4371--4378.

[54]

Hongke Zhao, Xinpeng Wu, Chuang Zhao, Lei Zhang, Haiping Ma, and Fan Cheng. 2021. CoEA: A cooperative--competitive evolutionary algorithm for bidirectional recommendations. IEEE Transactions on Evolutionary Computation, Vol. 26, 1 (2021), 28--42.

[55]

Hongke Zhao, Chuang Zhao, Xi Zhang, Nanlin Liu, Hengshu Zhu, Qi Liu, and Hui Xiong. 2023 b. An Ensemble Learning Approach with Gradient Resampling for Class-Imbalance Problems. INFORMS Journal on Computing (2023).

[56]

Lili Zhao, Sinno Jialin Pan, and Qiang Yang. 2017. A unified framework of active transfer learning for cross-system recommendation. Artificial Intelligence, Vol. 245 (2017), 38--55.

[57]

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1059--1068.

Digital Library

[58]

Feng Zhu, Chaochao Chen, Yan Wang, Guanfeng Liu, and Xiaolin Zheng. 2019. Dtcdr: A framework for dual-target cross-domain recommendation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1533--1542.

Digital Library

[59]

Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, and Xiaolin Zheng. 2020. A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation. In IJCAI. 3001--3008.

[60]

Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang, Ruobing Xie, Dongbo Xi, Xu Zhang, Leyu Lin, and Qing He. 2021a. Transfer-meta framework for cross-domain recommendation to cold-start users. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1813--1817.

Digital Library

[61]

Yongchun Zhu, Zhenwei Tang, Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, and Qing He. 2022b. Personalized transfer of user preferences for cross-domain recommendation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 1507--1515.

Digital Library

[62]

Ziwei Zhu, Yun He, Xing Zhao, and James Caverlee. 2021b. Popularity bias in dynamic recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2439--2449.

Digital Library

[63]

Ziwei Zhu, Yun He, Xing Zhao, and James Caverlee. 2022a. Evolution of Popularity Bias: Empirical Study and Debiasing. ArXiv, Vol. abs/2207.03372 (2022).

[64]

Ziwei Zhu, Jingu Kim, Trung Nguyen, Aish Fenton, and James Caverlee. 2021c. Fairness among new items in cold start recommender systems. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 767--776.

Digital Library

Cited By

Jiang JWu LHu ZWu RShen XZhao H(2025)Knowledge enhanced graph contrastive learning for match outcome predictionInformation Processing & Management10.1016/j.ipm.2024.10401062:3(104010)Online publication date: May-2025
https://doi.org/10.1016/j.ipm.2024.104010
Xiang ZZhao HZhao CHe MFan JBaeza-Yates RBonchi F(2024)Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender SystemsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671786(3507-3517)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671786
Zheng XLiu WChen CSu JLiao XHu MTan Y(2024)Mining User Consistent and Robust Preference for Unified Cross Domain RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.344658136:12(8758-8772)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3446581
Show More Cited By

Index Terms

Sequential Recommendation via an Adaptive Cross-domain Knowledge Decomposition
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation
RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Sequential recommendation methods play an irreplaceable role in recommender systems which can capture the users’ dynamic preferences from the behavior sequences. Despite their success, these works usually suffer from the sparsity problem commonly ...
Contrastive Multi-view Interest Learning for Cross-domain Sequential Recommendation
Cross-domain recommendation (CDR), which leverages information collected from other domains, has been empirically demonstrated to effectively alleviate data sparsity and cold-start problems encountered in traditional recommendation systems. However, ...
Domain ranking for cross domain collaborative filtering
UMAP'12: Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization

In recommendation systems a variation of the cold start problem is a situation where the target user has few-to-none item ratings belonging to the target domain (e.g., movies) to base recommendations on. One way to overcome this is by basing ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
,
Frank Hopfgartner
University of Koblenz, Germany
,
Mark Lee
University of Birmingham, UK
,
Michael Oakes
University of Birmingham, UK
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Min Zhang
Tsinghua University, China
,
Rodrygo Santos
Federal University of Minas Gerais, Brazil

Copyright © 2023 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 the author(s) 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].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 October 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Natural Science Foundation of China

Conference

CIKM '23

Sponsor:

CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2023

Birmingham, United Kingdom

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
387
Total Downloads

Downloads (Last 12 months)185
Downloads (Last 6 weeks)13

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Jiang JWu LHu ZWu RShen XZhao H(2025)Knowledge enhanced graph contrastive learning for match outcome predictionInformation Processing & Management10.1016/j.ipm.2024.10401062:3(104010)Online publication date: May-2025
https://doi.org/10.1016/j.ipm.2024.104010
Xiang ZZhao HZhao CHe MFan JBaeza-Yates RBonchi F(2024)Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender SystemsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671786(3507-3517)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671786
Zheng XLiu WChen CSu JLiao XHu MTan Y(2024)Mining User Consistent and Robust Preference for Unified Cross Domain RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.344658136:12(8758-8772)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3446581
Zhao CZhao HZhou XLi X(2024)Enhancing Precision Drug Recommendations via In-Depth Exploration of Motif RelationshipsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.343777536:12(8164-8178)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3437775
Gao XMeng DSun XWang WZhu HKong C(2024)STRec: Social-Augmented Time-Aware Cross-Domain Sequential Recommendation2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)10.1109/ISPA63168.2024.00070(502-509)Online publication date: 30-Oct-2024
https://doi.org/10.1109/ISPA63168.2024.00070

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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten