Citation: | Lianyong QI, Weiyi ZHONG, Chunhua HU, et al., “Correlation-aware Multi-dimensional Service Quality Prediction and Recommendation with Privacy-preservation in IoT,” Chinese Journal of Electronics, vol. x, no. x, article no. , xxxx doi: 10.23919/cje.2023.00.112 |
[1] |
Y. Chen, J. Zhao, X. K. Zhou, et al., “A distributed game theoretical approach for credibility-guaranteed multimedia data offloading in MEC,” Information Sciences, vol. 644, article no. 119306, 2023. doi: 10.1016/j.ins.2023.119306
|
[2] |
F. Z. Liu, J. W. Huang, and X. B. Wang, “Joint task offloading and resource allocation for device-edge-cloud collaboration with subtask dependencies,” IEEE Transactions on Cloud Computing, vol. 11, no. 3, pp. 3027–3039, 2023. doi: 10.1109/TCC.2023.3251561
|
[3] |
H. H. Gao, Y. S. Xu, Y. Y. Yin, et al., “Context-aware QoS prediction with neural collaborative filtering for internet-of-things services,” IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4532–4542, 2020. doi: 10.1109/JIOT.2019.2956827
|
[4] |
Z. M. Zhang, R. Yin, and H. S. Ning, “Internet of brain, thought, thinking, and creation,” Chinese Journal of Electronics, vol. 31, no. 6, pp. 1025–1042, 2022. doi: 10.1049/cje.2021.00.236
|
[5] |
Y. Chen, J. Zhao, J. T. Hu, et al., “Distributed task offloading and resource purchasing in NOMA-enabled mobile edge computing: Hierarchical game theoretical approaches,” ACM Transactions on Embedded Computing Systems, in press, 2023.
|
[6] |
L. H. Kong, J. L. Tan, J. Q. Huang, et al., “Edge-computing-driven internet of things: A survey,” ACM Computing Surveys, vol. 55, no. 8, article no. 174, 2023. doi: 10.1145/3555308
|
[7] |
H. Yin, X. Zhang, H. H. Liu, et al., “Edge provisioning with flexible server placement,” IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 4, pp. 1031–1045, 2017. doi: 10.1109/TPDS.2016.2604803
|
[8] |
E. Fazeldehkordi and T. M. Grønli, “A survey of security architectures for edge computing-based IoT,” IoT, vol. 3, no. 3, pp. 332–365, 2022. doi: 10.3390/iot3030019
|
[9] |
L. Y. Qi, Y. W. Liu, Y. L. Zhang, et al., “Privacy-aware point-of-interest category recommendation in internet of things,” IEEE Internet of Things Journal, vol. 9, no. 21, pp. 21398–21408, 2022. doi: 10.1109/JIOT.2022.3181136
|
[10] |
D. Goldberg, D. Nichols, B. M. Oki, et al., “Using collaborative filtering to weave an information tapestry,” Communications of the ACM, vol. 35, no. 12, pp. 61–70, 1992. doi: 10.1145/138859.138867
|
[11] |
Y. Chen, J. T. Hu, J. Zhao, et al., “QoS-aware computation offloading in LEO satellite edge computing for IoT: A game-theoretical approach,” Chinese Journal of Electronics, in press, 2023.
|
[12] |
F. Wang, G. S. Li, Y. L. Wang, et al., “Privacy-aware traffic flow prediction based on multi-party sensor data with zero trust in smart city,” ACM Transactions on Internet Technology (TOIT), vol. 23, no. 3, article no. 44, 2023. doi: 10.1145/3511904
|
[13] |
J. W. Huang, J. Y. Wan, B. F. Lv, et al., “Joint computation offloading and resource allocation for edge-cloud collaboration in internet of vehicles via deep reinforcement learning,” IEEE Systems Journal, vol. 17, no. 2, pp. 2500–2511, 2023. doi: 10.1109/JSYST.2023.3249217
|
[14] |
A. K. Junejo, I. A. Jokhio, and T. Jan, “A multi-dimensional and multi-factor trust computation framework for cloud services,” Electronics, vol. 11, no. 13, article no. 1932, 2022. doi: 10.3390/electronics11131932
|
[15] |
L. H. Ding, J. X. Liu, G. S. Kang, et al., “Joint QoS prediction for web services based on deep fusion of features,” IEEE Transactions on Network and Service Management, in press, 2023.
|
[16] |
S. Zhang, H. B. Luo, J. L. Li, et al., “Hierarchical soft slicing to meet multi-dimensional QoS demand in cache-enabled vehicular networks,” IEEE Transactions on Wireless Communications, vol. 19, no. 3, pp. 2150–2162, 2020. doi: 10.1109/TWC.2019.2962798
|
[17] |
S. G. Wang, Y. Ma, B. Cheng, et al., “Multi-dimensional QoS prediction for service recommendations,” IEEE Transactions on Services Computing, vol. 12, no. 1, pp. 47–57, 2019. doi: 10.1109/TSC.2016.2584058
|
[18] |
Y. W. Zhang, X. F. Ai, Q. He, et al., “Personalized quality centric service recommendation,” in 15th International Conference on Service-Oriented Computing, Malaga, Spain, pp. 528–544, 2017.
|
[19] |
H. Q. Lian, J. H. Li, H. Wu, et al., “Towards effective personalized service QoS prediction from the perspective of multi-task learning,” IEEE Transactions on Network and Service Management, in press, 2023.
|
[20] |
L. Li, M. Liu, W. M. Shen, et al., “Recommending mobile services with trustworthy QoS and dynamic user preferences via FAHP and ordinal utility function,” IEEE Transactions on Mobile Computing, vol. 19, no. 2, pp. 419–431, 2020. doi: 10.1109/TMC.2019.2896239
|
[21] |
C. Musto, M. De Gemmis, G. Semeraro, et al., “A multi-criteria recommender system exploiting aspect-based sentiment analysis of users’ reviews,” in Proceedings of the Eleventh ACM Conference on Recommender Systems, Como, Italy, pp. 321–325, 2017.
|
[22] |
H. Jin, X. F. Yao, and Y. Chen, “Correlation-aware QoS modeling and manufacturing cloud service composition,” Journal of Intelligent Manufacturing, vol. 28, no. 8, pp. 1947–1960, 2017. doi: 10.1007/s10845-015-1080-2
|
[23] |
Y. W. Zhang, G. M. Cui, S. G. Deng, et al., “Efficient query of quality correlation for service composition,” IEEE Transactions on Services Computing, vol. 14, no. 3, pp. 695–709, 2021. doi: 10.1109/TSC.2018.2830773
|
[24] |
C. Y. Yin, L. F. Shi, R. X. Sun, et al., “Improved collaborative filtering recommendation algorithm based on differential privacy protection,” The Journal of Supercomputing, vol. 76, no. 7, pp. 5161–5174, 2020. doi: 10.1007/s11227-019-02751-7
|
[25] |
Q. Tang and J. Wang, “Privacy-preserving friendship-based recommender systems,” IEEE Transactions on Dependable and Secure Computing, vol. 15, no. 5, pp. 784–796, 2018. doi: 10.1109/TDSC.2016.2631533
|
[26] |
S. B. Zhang, X. Li, Z. Y. Tan, et al., “A caching and spatial K-anonymity driven privacy enhancement scheme in continuous location-based services,” Future Generation Computer Systems, vol. 94, pp. 40–50, 2019. doi: 10.1016/j.future.2018.10.053
|
[27] |
X. L. Xu, Q. H. Huang, Y. W. Zhang, et al., “An LSH-based offloading method for ioMT services in integrated cloud-edge environment,” ACM Transactions on Multimedia Computing, vol. 16, no. 3S, article no. 94, 2021. doi: 10.1145/3408319
|
[28] |
H. H. Gao, Y. S. Xu, Y. Y. Yin, et al., “Context-aware QoS prediction with neural collaborative filtering for internet-of-things services,” IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4532–4542, 2020. doi: 10.1109/JIOT.2019.2956827.
|
[29] |
X. X. Chi, C. Yan, H. Wang, et al., “Amplified locality-sensitive hashing-based recommender systems with privacy protection,” Concurrency and Computation:Practice and Experience, vol. 34, no. 14, article no. e5681, 2022. doi: 10.1002/cpe.5681
|
[30] |
W. W. Gong, L. Y. Qi, and Y. W. Xu, “Privacy-aware multidimensional mobile service quality prediction and recommendation in distributed fog environment,” Wireless Communications and Mobile Computing, vol. 2018, article no. 3075849, 2018. doi: 10.1155/2018/3075849
|
[31] |
W. Y. Zhong, X. C. Yin, X. Y. Zhang, et al., “Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment,” Computer Communications, vol. 157, pp. 116–123, 2020. doi: 10.1016/j.comcom.2020.04.018
|
[32] |
Y. Chen, J. Zhao, Y. Wu, et al., “QoE-aware decentralized task offloading and resource allocation for end-edge-cloud systems: A game-theoretical approach,” IEEE Transactions on Mobile Computing, in press, 2022.
|
[33] |
M. W. Zhang, Y. M. Zhang, and G. Shen, “PPDDS: A privacy-preserving disease diagnosis scheme based on the secure mahalanobis distance evaluation model,” IEEE Systems Journal, vol. 16, no. 3, pp. 4552–4562, 2022. doi: 10.1109/JSYST.2021.3093415
|
[34] |
Y. J. Lai, T. Y. Liu, and C. L. Hwang, “TOPSIS for MODM,” European Journal of Operational Research, vol. 76, no. 3, pp. 486–500, 1994. doi: 10.1016/0377-2217(94)90282-8
|
[35] |
Z. B. Zheng, Y. L. Zhang, and M. R. Lyu, “Investigating QoS of real-world web services,” IEEE Transactions on Services Computing, vol. 7, no. 1, pp. 32–39, 2014. doi: 10.1109/TSC.2012.34
|
[36] |
J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” arXiv preprint, arXiv: 1301.7363, 2013.
|
[37] |
G. Linden, B. Smith, and J. York, “Amazon. com recommendations: Item-to-item collaborative filtering,” IEEE Internet Computing, vol. 7, no. 1, pp. 76–80, 2003. doi: 10.1109/MIC.2003.1167344
|
[38] |
L. Z. Zeng, B. Benatallah, A. H. H. Ngu, et al., “QoS-aware middleware for web services composition,” IEEE Transactions on Software Engineering, vol. 30, no. 5, pp. 311–327, 2004. doi: 10.1109/TSE.2004.11
|
[39] |
L. Y. Qi, X. Y. Zhang, W. C. Dou, et al., “A distributed locality-sensitive hashing-based approach for cloud service recommendation from multi-source data,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 11, pp. 2616–2624, 2017. doi: 10.1109/JSAC.2017.2760458
|
[40] |
Y. W. Liu, H. P. Wu, K. Rezaee, et al., “Interaction-enhanced and time-aware graph convolutional network for successive point-of-interest recommendation in traveling enterprises,” IEEE Transactions on Industrial Informatics, vol. 19, no. 1, pp. 635–643, 2023. doi: 10.1109/TII.2022.3200067
|
[41] |
Y. Zhang, C. Yin, Z. Lu, et al., “Recurrent tensor factorization for time-aware service recommendation,” Applied Soft Computing, vol. 85, article no. 105762, 2019. doi: 10.1016/j.asoc.2019.105762
|
[42] |
Y. Chen, W. Gu, J. J. Xu, et al., “Dynamic task offloading for digital twin-empowered mobile edge computing via deep reinforcement learning,” China Communications, in press, 2023.
|