research-article

A Novel Blockchain-based Responsible Recommendation System for Service Process Creation and Recommendation

Authors:

Quan Z. ShengAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 15, Issue 4

Article No.: 79, Pages 1 - 24

https://doi.org/10.1145/3643858

Published: 29 July 2024 Publication History

Abstract

Service composition platforms play a crucial role in creating personalized service processes. Challenges, including the risk of tampering with service data during service invocation and the potential single point of failure in centralized service registration centers, hinder the efficient and responsible creation of service processes. This paper presents a novel framework called Context-Aware Responsible Service Process Creation and Recommendation (SPCR-CA), which incorporates blockchain, Recurrent Neural Networks (RNNs), and a Skip-Gram model holistically to enhance the security, efficiency, and quality of service process creation and recommendation. Specifically, the blockchain establishes a trusted service provision environment, ensuring transparent and secure transactions between services and mitigating the risk of tampering. The RNN trains responsible service processes, contextualizing service components and producing coherent recommendations of linkage components. The Skip-Gram model trains responsible user-service process records, generating semantic vectors that facilitate the recommendation of similar service processes to users. Experiments using the Programmable-Web dataset demonstrate the superiority of the SPCR-CA framework to existing benchmarks in precision and recall. The proposed framework enhances the reliability, efficiency, and quality of service process creation and recommendation, enabling users to create responsible and tailored service processes. The SPCR-CA framework offers promising potential to provide users with secure and user-centric service creation and recommendation capabilities.

References

[1]

Neha Agarwal, Geeta Sikka, and Lalit Kumar Awasthi. 2023. WGSDMM+GA: A genetic algorithm-based service clustering methodology assimilating dirichlet multinomial mixture model with word embedding. Fut. Gener. Comput. Syst. 145, C (2023), 254–266.

Digital Library

[2]

Nuri Almarimi, Ali Ouni, Salah Bouktif, Mohamed Wiem Mkaouer, Raula Kula, and Mohamed Saied. 2019. Web service API recommendation for automated mashup creation using multi-objective evolutionary search. Appl. Soft Comput. 85 (102019), 105830.

Digital Library

[3]

Ankur O. Bang, Udai Pratap Rao, and Amit A. Bhusari. 2022. A comprehensive study of security issues and research challenges in different layers of service-oriented IoT architecture. Cyber Secur. Digit. Forens. (2022), 1–43.

[4]

Yaoming Cai, Zijia Zhang, Pedram Ghamisi, Zhihua Cai, Xiaobo Liu, and Yao Ding. 2023. Fully linear graph convolutional networks for semi-supervised and unsupervised classification. ACM Trans. Intell. Syst. Technol. 14, 3 (Apr.2023), 1–23.

Digital Library

[5]

Buqing Cao, Xiaoqing Frank Liu, M. D. Mahfuzer Rahman, Bing Li, Jianxun Liu, and Mingdong Tang. 2020. Integrated content and network-based service clustering and Web APIs recommendation for mashup development. IEEE Trans. Serv. Comput. 13, 1 (2020), 99–113.

[6]

Buqing Cao, Mi Peng, Yueying Qing, Jianxun Liu, Guosheng Kang, Bing Li, and Kenneth K. Fletcher. 2022. Web API recommendation via combining graph attention representation and deep factorization machines quality prediction. Concurr. Comput.: Pract. Exp. 34, 21 (2022), e7069.

[7]

M. Chen, T. Gao, Z. Zhang, X. Ji, and Q. Tang. 2021. Service process recommendation based on user multiple interests. Jisuanji Jicheng Zhizao Xitong/Comput. Integr. Manufact. Syst. 27, 9 (2021), 2701–2707.

[8]

Wenqing Chen and Ting Yang. 2023. A recommendation system of personalized resource reliability for online teaching system under large-scale user access. Mobile Netw. Appl. (2023), 1–12.

[9]

Bo Cheng, Zhongyi Zhai, Shuai Zhao, and Junliang Chen. 2017. LSMP: A lightweight service mashup platform for ordinary users. IEEE Commun. Mag. 55, 4 (2017), 116–123.

Digital Library

[10]

Shuiguang Deng, Dongjing Wang, Ying Li, Bin Cao, Jianwei Yin, Zhaohui Wu, and Mengchu Zhou. 2017. A recommendation system to facilitate business process modeling. IEEE Trans. Cybernet. 47, 6 (2017), 1380–1394.

[11]

Shuiguang Deng, Hongyue Wu, Javid Taheri, Albert Y. Zomaya, and Zhaohui Wu. 2016. Cost performance driven service mashup: A developer perspective. IEEE Trans. Parallel Distrib. Syst. 27, 8 (2016), 2234–2247.

Digital Library

[12]

Kai Dong, Yakun Zhang, Yuchen Zhao, Daoming Li, Zhen Ling, Wenjia Wu, and Xiaorui Zhu. 2022. Real-time execution of trigger-action connection for home internet-of-things. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’22). IEEE, 1489–1498.

Digital Library

[13]

Li Duan, Tieliang Gao, Wei Ni, and Wei Wang. 2021. A hybrid intelligent service recommendation by latent semantics and explicit ratings. Int. J. Intell. Syst. 36 (082021), 7867–7894.

Digital Library

[14]

Tieliang Gao, Bo Cheng, Junliang Chen, and Ming Chen. 2017. Enhancing collaborative filtering via topic model integrated uniform euclidean distance. Chin. Commun. 14, 11 (2017), 48–58.

[15]

Qi Gu, Jian Cao, and Qianyang Peng. 2016. Service package recommendation for mashup creation via mashup textual description mining. In Proceedings of the IEEE International Conference on Web Services (ICWS’16). 452–459.

[16]

Keman Huang, Yushun Fan, and Wei Tan. 2014. Recommendation in an evolving service ecosystem based on network prediction. IEEE Trans. Autom. Sci. Eng. 11, 3 (2014), 906–920.

[17]

Guosheng Kang, Jianxun Liu, Yong Xiao, Buqing Cao, Yu Xu, and Manliang Cao. 2021. Neural and attentional factorization machine-based Web API recommendation for mashup development. IEEE Trans. Netw. Serv. Manage. 18, 4 (2021), 4183–4196.

[18]

Guosheng Kang, Jianxun Liu, Yong Xiao, Yingcheng Cao, Buqing Cao, and Min Shi. 2022. Web services clustering via exploring unified content and structural semantic Representation. IEEE Trans. Netw. Serv. Manage. 19, 4 (2022), 4082–4096.

[19]

Cheng-Te Li, Cheng Hsu, and Yang Zhang. 2022. FairSR: Fairness-aware sequential recommendation through multi-task learning with preference graph embeddings. ACM Trans. Intell. Syst. Technol. 13, 1 (2022), 1–21.

Digital Library

[20]

Sixian Lian and Mingdong Tang. 2021. API recommendation for mashup creation based on neural graph collaborative filtering. Connect. Sci. 34 (2021), 124–138.

[21]

Mingyi Liu, Zhiying Tu, Hanchuan Xu, Xiaofei Xu, and Zhongjie Wang. 2023. DySR: A dynamic graph neural network based service bundle recommendation model for mashup creation. IEEE Trans. Serv. Comput. 16, 4 (2023), 2592–2605.

[22]

Xuelian Long and James Joshi. 2013. A HITS-based POI recommendation algorithm for location-based social networks. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’13). 642–647.

Digital Library

[23]

Yanzhang Lyu, Hongzhi Yin, Jun Liu, Mengyue Liu, Huan Liu, and Shizhuo Deng. 2021. Reliable recommendation with review-level explanations. In Proceedings of the IEEE 37th International Conference on Data Engineering (ICDE’21). IEEE, 1548–1558.

[24]

You Ma, Shangguang Wang, Patrick C. K. Hung, Ching-Hsien Hsu, Qibo Sun, and Fangchun Yang. 2016. A highly accurate prediction algorithm for unknown Web service QoS values. IEEE Trans. Serv. Comput. 9, 4 (2016), 511–523.

[25]

Yayu Ni, Yushun Fan, Wei Tan, Keman Huang, and Jing Bi. 2015. NCSR: Negative-connection-aware service recommendation for large sparse service network. IEEE Trans. Autom. Sci. Eng. 13, 2 (2016), 579–590.

[26]

Seong-Taek Park and Chang Liu. 2020. A study on topic models using LDA and Word2Vec in travel route recommendation: focus on convergence travel and tours reviews. Personal Ubiquitous Comput. 26, 2 (Apr 2022), 429–445.

Digital Library

[27]

Lianyong Qi, Qiang He, Feifei Chen, Xuyun Zhang, Wanchun Dou, and Qiang Ni. 2022. Data-driven Web APIs recommendation for building Web applications. IEEE Trans. Big Data 8, 3 (2022), 685–698.

[28]

Shaojian Qu, Lei Xu, Sachin Mangla, Felix Chan, Jianli Zhu, and Sobhan Arisian. 2022. Matchmaking in reward-based crowdfunding platforms: A hybrid machine learning approach. Int. J. Prod. Res. 60, 24 (2022), 7551–7571.

[29]

Lifang Ren and Wenjian Wang. 2022. A granular SVM-based method for top-n Web services recommendation. IEEE Trans. Serv. Comput. 15, 1 (2022), 457–469.

[30]

Omair Shafiq, Reda Alhajj, and Jon G. Rokne. 2014. Frequent pattern mining using semantic FP-growth for effective Web service ranking. In Proceedings of the IEEE International Conference on Web Services. (2014), 725–727.

Digital Library

[31]

E. Hikmawati, N. U. Maulidevi, and K. Surendro. 2021. Minimum threshold determination method based on dataset characteristics in association rule mining. Journal of Big Data 8 (2021), 1–17.

[32]

Mai Shawkat, Mahmoud Badawi, Sally Elghamrawy, Reham Arnous, and Ali El-desoky. 2022. An optimized FP-growth algorithm for discovery of association rules. J. Supercomput. 78, 4 (03 2022), 5479–5506.

Digital Library

[33]

Lei-lei Shi, Lu Liu, Liang Jiang, Rongbo Zhu, and John Panneerselvam. 2021. QoS prediction for smart service management and recommendation based on the location of mobile users. Neurocomputing 471, C (11 2022), 12–20.

Digital Library

[34]

Amanveer Singh. 2023. Information retrieval based on the sequential hypertext induced topic search in Web pages. In Proceedings of the International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE’23). IEEE, 1–5.

[35]

Lei Wang, Tianyi Gao, Bin Zhou, Hongtao Tang, and Feng Xiang. 2021. Manufacturing service recommendation method toward industrial internet platform considering the cooperative relationship among enterprises. Expert Syst. Appl. 192, C (122021), 116391.

Digital Library

[36]

Shangguang Wang, You Ma, Bo Cheng, Fangchun Yang, and Rong N. Chang. 2019. Multi-dimensional QoS prediction for service recommendations. IEEE Trans. Serv. Comput. 12, 1 (2019), 47–57.

[37]

Shangguang Wang, Xilu Zhu, and Fangchun Yang. 2014. Efficient QoS management for QoS-aware Web service composition. Int. J. Web Grid Serv. 10, 1 (012014), 1–23.

Digital Library

[38]

Yuan Wei, Viet Quoc, Nguyen Hung, He Tieke, Chen Liang, and Yin Hongzhi. 2023. Manipulating federated recommender systems: Poisoning with synthetic users and its countermeasures. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1690–1699.

[39]

Bofei Xia, Yushun Fan, Wei Tan, Keman Huang, Jia Zhang, and Cheng Wu. 2015. Category-aware API clustering and distributed recommendation for automatic mashup creation. IEEE Trans. Serv. Comput. 8, 5 (2015), 674–687.

[40]

Yueshen Xu, Zhiying Wang, Honghao Gao, Zhiping Jiang, Yuyu Yin, and Rui Li. 2023. Towards machine-learning-driven effective mashup recommendations from big data in mobile networks and the Internet-of-Things. Digit. Commun. Netw. 9, 1 (2023), 138–145.

[41]

Shu-Rong Yan, Sina Pirooznia, Arash Heidari, Nima Jafari Navimipour, and Mehmet Unal. 2022. Implementation of a product-recommender system in an IoT-based smart shopping using fuzzy logic and apriori algorithm. IEEE Trans. Eng. Manage. 71 (2014), 4940–4954.

[42]

Wei Yuan, Hongzhi Yin, Fangzhao Wu, Shijie Zhang, Tieke He, and Hao Wang. 2023. Federated unlearning for on-device recommendation. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining. 393–401.

Digital Library

[43]

Neng Zhang, Jian Wang, Keqing He, Zheng Li, and Yiwang Huang. 2019. Mining and clustering service goals for RESTful service discovery. Knowl. Inf. Syst. 58, 3 (03 2019), 669–700.

Digital Library

[44]

Praveen Kumar, B. T. Padmavathy, S. U. Muthunagai, and D. Paulraj. 2023. An optimized fuzzy based FP-growth algorithm for mining temporal data. Journal of Intelligent & Fuzzy Systems Preprint (2023), 1–11.

Cited By

Xue HWang YTang Q(2024)Dynamic analysis of malicious behavior propagation based on feature selection in software networkFrontiers in Physics10.3389/fphy.2024.149320912Online publication date: 1-Nov-2024
https://doi.org/10.3389/fphy.2024.1493209
Li JXue HTang QWang HGao T(2024)SABTR: semantic analysis-based tourism recommendationFrontiers in Physics10.3389/fphy.2024.149136512Online publication date: 17-Oct-2024
https://doi.org/10.3389/fphy.2024.1491365

Index Terms

A Novel Blockchain-based Responsible Recommendation System for Service Process Creation and Recommendation
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Association rules
2. Security and privacy
  1. Systems security

Recommendations

An integrated service recommendation approach for service-based system development
Highlights
- Service recommendation for SBS (service-based system) development.
- Measuring ...
Abstract
With the wide adoption of service-oriented computing and cloud computing, service-based systems (SBSs), a kind of software systems that can offer certain functionalities by leveraging one or more Web services, become increasingly ...
Composite Service Recommendation Based on Bayes Theorem

The number of web services increased exponentially in the past decade. Identifying a set of best candidates from vast services is the first step of composite service recommendation. Much of the previous work focused on the accuracy and efficiency in ...
A Collaborative Filtering Method for Personalized Preference-Based Service Recommendation
ICWS '15: Proceedings of the 2015 IEEE International Conference on Web Services

Existing service recommendation methods, that employ memory-based collaborative filtering (CF) techniques, compute the similarity between users or items using nonfunctional attribute values obtained at service invocation. However, using these ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 15, Issue 4

August 2024

563 pages

EISSN:2157-6912

DOI:10.1145/3613644

Editor:
Huan Liu
Arizona State University, USA

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 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 July 2024

Online AM: 02 March 2024

Accepted: 03 January 2024

Revised: 13 November 2023

Received: 30 June 2023

Published in TIST Volume 15, Issue 4

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Fundamental Research Funds for the Central Universities of China
National Natural Science Foundation of China
Open Foundation of Information Security Evaluation Center of Civil Aviation, Civil Aviation University of China
Australian Research Council (ARC) Discovery Project ARC
Soft Science Research Project of Henan Province

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
233
Total Downloads

Downloads (Last 12 months)233
Downloads (Last 6 weeks)47

Reflects downloads up to 05 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Xue HWang YTang Q(2024)Dynamic analysis of malicious behavior propagation based on feature selection in software networkFrontiers in Physics10.3389/fphy.2024.149320912Online publication date: 1-Nov-2024
https://doi.org/10.3389/fphy.2024.1493209
Li JXue HTang QWang HGao T(2024)SABTR: semantic analysis-based tourism recommendationFrontiers in Physics10.3389/fphy.2024.149136512Online publication date: 17-Oct-2024
https://doi.org/10.3389/fphy.2024.1491365

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.

Full Text

View this article in Full Text.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents