AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (1.6 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

A Deep Neural Collaborative Filtering Based Service Recommendation Method with Multi-Source Data for Smart Cloud-Edge Collaboration Applications

Alibaba Business School, Hangzhou Normal University, Hangzhou 311121, China
Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science and Technology, Shouguang 262700, China
School of Computer Science, Qufu Normal University, Rizhao 276827, China
School of Information Engineering, Huzhou University, Huzhou 313000, China
Show Author Information

Abstract

Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices. However, the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods. In view of these challenges, we propose a deep neural collaborative filtering based service recommendation method with multi-source data (i.e., NCF-MS) in this paper, which adopts the cloud-edge collaboration computing paradigm to build recommendation model. More specifically, the Stacked Denoising Auto Encoder (SDAE) module is adopted to extract user/service features from auxiliary user profiles and service attributes. The Multiple Layer Perceptron (MLP) module is adopted to integrate the auxiliary user/service features to train the recommendation model. Finally, we evaluate the effectiveness of the NCF-MS method on three public datasets. The experimental results show that our proposed method achieves better performance than existing methods.

Keywords

deep neural collaborative filteringmulti-source datacloud-edge collaboration applicationstacked denoising auto encodermultiple layer perceptron

References

[1]

X. Zhou, Y. Hu, J. Wu, W. Liang, J. Ma, and Q. Jin, Distribution bias aware collaborative generative adversarial network for imbalanced deep learning in industrial IoT, IEEE Trans. Ind. Inf., vol. 19, no. 1, pp. 570–580, 2023.
Crossref Google Scholar
[2]

L. Qi, Q. He, F. Chen, X. Zhang, W. Dou, and Q. Ni, Data-driven web APIs recommendation for building web applications, IEEE Trans. Big Data, vol. 8, no. 3, pp. 685–698, 2022.
Crossref Google Scholar
[3]

F. Wang, H. Zhu, G. Srivastava, S. Li, M. R. Khosravi, and L. Qi, Robust collaborative filtering recommendation with user-item-trust records, IEEE Trans. Comput. Soc. Syst., vol. 9, no. 4, pp. 986–996, 2022.
Crossref Google Scholar
[4]

Y. Liu, A. Pei, F. Wang, Y. Yang, X. Zhang, H. Wang, H. Dai, L. Qi, and R. Ma, An attention-based category-aware GRU model for the next POI recommendation, Int. J. Intell. Syst., vol. 36, no. 7, pp. 3174–3189, 2021.
Crossref Google Scholar
[5]

W. Lin, X. Zhang, L. Qi, W. Li, S. Li, V. S. Sheng, and S. Nepal, Location-aware service recommendations with privacy-preservation in the Internet of Things, IEEE Trans. Comput. Soc. Syst., vol. 8, no. 1, pp. 227–235, 2021.
Crossref Google Scholar
[6]
C. Wu, F. Wu, Y. Huang, and X. Xie, Personalized news recommendation: Methods and challenges, arXiv preprint arXiv: 2106.08934, 2021.
[7]

S. Jiang, X. Qian, T. Mei, and Y. Fu, Personalized travel sequence recommendation on multi-source big social media, IEEE Trans. Big Data, vol. 2, no. 1, pp. 43–56, 2016.
Crossref Google Scholar
[8]

H. Liu, Y. Tong, J. Han, P. Zhang, X. Lu, and H. Xiong, Incorporating multi-source urban data for personalized and context-aware multi-modal transportation recommendation, IEEE Trans. Knowl. Data Eng., vol. 34, no. 2, pp. 723–735, 2022.
Crossref Google Scholar
[9]

G. Zhao, T. Liu, X. Qian, T. Hou, H. Wang, X. Hou, and Z. Li, Location recommendation for enterprises by multi-source urban big data analysis, IEEE Trans. Serv. Comput., vol. 13, no. 6, pp. 1115–1127, 2020.
Google Scholar
[10]

L. Qi, W. Lin, X. Zhang, W. Dou, X. Xu, and J. Chen, A correlation graph based approach for personalized and compatible web APIs recommendation in mobile APP development, IEEE Trans. Knowl. Data Eng., vol. 35, no. 6, pp. 5444–5457, 2023.
Google Scholar
[11]

S. Wu, S. Shen, X. Xu, Y. Chen, X. Zhou, D. Liu, X. Xue, and L. Qi, Popularity-aware and diverse web APIs recommendation based on correlation graph, IEEE Trans. Comput. Soc. Syst., vol. 10, no. 2, pp. 771–782, 2023.
Crossref Google Scholar
[12]

X. Zhou, W. Liang, K. Yan, W. Li, K. I. K. Wang, J. Ma, and Q. Jin, Edge-enabled two-stage scheduling based on deep reinforcement learning for Internet of Everything, IEEE Internet Things J., vol. 10, no. 4, pp. 3295–3304, 2022.
Crossref Google Scholar
[13]

X. Xu, J. Gu, H. Yan, W. Liu, L. Qi, and X. Zhou, Reputation-aware supplier assessment for blockchain-enabled supply chain in industry 4.0, IEEE Trans. Ind. Inform., vol. 19, no. 4, pp. 5485–5494, 2023.
Crossref Google Scholar
[14]

L. Qi, Y. Yang, X. Zhou, W. Rafique, and J. Ma, Fast anomaly identification based on multiaspect data streams for intelligent intrusion detection toward secure industry 4.0, IEEE Trans. Ind. Inform., vol. 18, no. 9, pp. 6503–6511, 2022.
Crossref Google Scholar
[15]

Z. Li, X. Xu, T. Hang, H. Xiang, Y. Cui, L. Qi, and X. Zhou, A knowledge-driven anomaly detection framework for social production system, IEEE Trans. Comput. Soc. Syst., vol. 1, no. 99, pp. 1–14, 2022.
Crossref Google Scholar
[16]

X. Zhou, X. Xu, W. Liang, Z. Zeng, and Z. Yan, Deep-learning-enhanced multitarget detection for end–edge–cloud surveillance in smart IoT, IEEE Internet Things J., vol. 8, no. 16, pp. 12588–12596, 2021.
Crossref Google Scholar
[17]
X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T. S. Chua, Neural collaborative filtering, in Proc. 26th Int. Conf. World Wide Web, 2017, pp. 173–182.
Crossref
[18]

L. Qi, Z. Zhou, J. Yu, and Q. Liu, Data-sparsity tolerant web service recommendation approach based on improved collaborative filtering, IEICE Trans. Inf. & Syst., vol. E100.D, no. 9, pp. 2092–2099, 2017.
Crossref Google Scholar
[19]

S. Meng, L. Qi, Q. Li, W. Lin, X. Xu, and S. Wan, Privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems, Future Gener. Comput. Syst., vol. 96, pp. 324–335, 2019.
Crossref Google Scholar
[20]

X. Zhou, X. Yang, J. Ma, and K. I. K. Wang, Energy-efficient smart routing based on link correlation mining for wireless edge computing in IoT, IEEE Internet Things J., vol. 9, no. 16, pp. 14988–14997, 2022.
Crossref Google Scholar
[21]

Y. Jia, B. Liu, W. Dou, X. Xu, X. Zhou, L. Qi, and Z. Yan, CroApp: A CNN-based resource optimization approach in edge computing environment, IEEE Trans. Ind. Inform., vol. 18, no. 9, pp. 6300–6307, 2022.
Crossref Google Scholar
[22]

X. Xu, H. Tian, X. Zhang, L. Qi, Q. He, and W. Dou, DisCOV: Distributed COVID-19 detection on X-ray images with edge-cloud collaboration, IEEE Trans. Serv. Comput., vol. 15, no. 3, pp. 1206–1219, 2022.
Crossref Google Scholar
[23]
T. T. Nguyen, On the edge and cloud: Recommendation systems with distributed machine learning, in Proc. 2021 Int. Conf. Information Technology (ICIT), Amman, Jordan, 2021, pp. 929–934.
Crossref
[24]

C. Li and K. He, CBMR: An optimized MapReduce for item-based collaborative filtering recommendation algorithm with empirical analysis, Concurr. Comput. Pract. Exp., vol. 29, no. 10, p. e4092, 2017.
Crossref Google Scholar
[25]

F. Ortega, A. Hernando, J. Bobadilla, and J. H. Kang, Recommending items to group of users using matrix factorization based collaborative filtering, Inf. Sci., vol. 345, pp. 313–324, 2016.
Crossref Google Scholar
[26]
R. Salakhutdinov, A. Mnih, and G. Hinton, Restricted Boltzmann machines for collaborative filtering, in Proc. 24th Int. Conf. Machine learning, Corvalis, OR, USA, 2007, pp. 791–798.
Crossref
[27]

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. A. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res., vol. 11, pp. 3371–3408, 2010.
Google Scholar
[28]
W. J. Li, D. Y. Yeung, and Z. Zhang, Generalized latent factor models for social network analysis, in Proc. Twenty-Second Int. joint Conf. Artificial Intelligence, Barcelona, Spain, 2011, pp. 1705–1710.
[29]
L. Hu, J. Cao, G. Xu, L. Cao, Z. Gu, and C. Zhu, Personalized recommendation via cross-domain triadic factorization, in Proc. 22nd Int. Conf. World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 595–606.
Crossref
[30]
Z. H. Deng, L. Huang, C. D. Wang, J. H. Lai, and P. S. Yu, DeepCF: A unified framework of representation learning and matching function learning in recommender system, arXiv preprint arXiv: 1901.04704, 2019.
[31]
S. Sedhain, A. K. Menon, S. Sanner, and L. Xie, AutoRec: autoencoders meet collaborative filtering, in Proc. 24th Int. Conf. World Wide Web, Florence, Italy, 2015, pp. 111–112.
Crossref
[32]
L. Zheng, V. Noroozi, and P. S. Yu, Joint deep modeling of users and items using reviews for recommendation, in Proc. Tenth ACM Int. Conf. Web Search and Data Mining, Cambridge, United Kingdom, 2017, pp. 425–434.
Crossref
[33]

C. Chen, M. Zhang, Y. Liu, and S. Ma, Neural attentional rating regression with review-level explanations, in Proc. 2018 World Wide Web Conference Lyon, France , 2018, pp. 1583–1592.
Crossref Google Scholar
[34]
C. Wu, F. Wu, T. Qi, and Y. Huang, SentiRec: Sentiment diversity-aware neural news recommendation, in Proc. 1st Conf. Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th Int. Joint Conf. on Natural Language Processing, Suzhou, China, 2020, pp. 44−53.
[35]
One-hot Encoding, https://www.cs.toronto.edu/guerzhoy/321/lec/W04/onehot.pdf, 2023.
[36]
Banco Santander, https://www.santander.com/en/landing-pages/dataprotection-policy, 2023.
[37]
Amazon Review Data, https://nijianmo.github.io/amazon/index.html, 2018.
Tsinghua Science and Technology
Volume 29 Issue 3,
June 2024
Pages 897-910
DOI: 10.26599/TST.2023.9010050
Cite this article:
Lin W, Zhu M, Zhou X, et al. A Deep Neural Collaborative Filtering Based Service Recommendation Method with Multi-Source Data for Smart Cloud-Edge Collaboration Applications. Tsinghua Science and Technology, 2024, 29(3): 897-910. https://doi.org/10.26599/TST.2023.9010050

1094

Views

313

Downloads

14

Crossref

20

Web of Science

23

Scopus

0

CSCD

Altmetrics
Received: 04 April 2023
Revised: 11 May 2023
Accepted: 24 May 2023
Published: 04 December 2023
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return