Skip to main content
SpringerLink
Log in

Location-based and Time-aware Service Recommendation in Mobile Edge Computing

  • Published:
International Journal of Parallel Programming Aims and scope Submit manuscript

Abstract

With the rapid development of Internet of Things, mobile edge computing which provides physical resources closer to end users has gained considerable popularity in academic and industrial field. As the number of edge server increases, accessing effective edge services fast is an urgent problem to be solved. In this paper, we mainly focus on the cold-start problem for service recommendation based on location of users and services. Address this conundrum, we propose a service recommendation method based on collaborative filtering (CF) and location, by comprehensively considering the characteristic of services at the edge, mobility and demands of users at different time periods. In detail, we synthesize the service characteristics of each dimension in different time slices through multidimensional weighting method at first. Then We further introduce the idea of Inverse CF Rec to the traditional CF and predict the lost quality of service (QoS) to solve the problem of sparse data. Finally, a recommendation algorithm based on predicted QoS and user geographic location is proposed to recommend appropriate services to users. The experimental results show that our multidimensional inverse similarity recommendation algorithm based on time-aware collaborative filtering (MDITCF) outperforms Inverse CF Rec in terms of the accuracy of recommendation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Availability of Data and Materials

https://wsdream.github.io/

Code Availability

Not applicable.

References

  1. Wang, S., Zhao, Y., Xu, J., Yuan, J., Hsu, C.H.: Edge server placement in mobile edge computing. J. Parallel Distrib. Comput. 127, 160–168 (2019)

    Article  Google Scholar 

  2. Ahmed, A., Ahmed, E.: A survey on mobile edge computing. In: 2016 10th IEEE International Conference on Intelligent Systems and Control, pp. 1–8 (2016)

  3. Zheng, Z., Ma, H., Lyu, M.R., King, I.: Qos-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)

    Article  Google Scholar 

  4. Kang, G., Tang, M., Liu, J., Liu, X., Cao, B.: Diversifying web service recommendation results via exploring service usage history. IEEE Trans. Serv. Comput. 9(4), 566–579 (2016)

    Article  Google Scholar 

  5. Wang, S., Zhao, Y., Huang, L., Xu, J., Hsu, C.H.: QoS prediction for service recommendations in mobile edge computing. J. Parallel Distrib. Comput. 127, 134–144 (2019)

    Article  Google Scholar 

  6. Herlocker, J.L.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retrieval 5, 287–310 (2002)

    Article  Google Scholar 

  7. Li, K., Long, Y., Lan, H., Yin, W., Pengju, M., Chuanbin, L., Yujia, Z.: A personalized QoS prediction approach for cps service recommendation based on reputation and location-aware collaborative filtering. Sensors 18(5) (2018)

  8. Arshad, R., Elsawy, H., Sorour, S., Alnaffouri, T. Y., Alouini, M. S.: Handover management in dense cellular networks: A stochastic geometry approach. In: 2016 IEEE International Conference on Communications, Kuala Lumpur, pp. 1–7 (2016)

  9. Li, S., Wen, J., Luo, F.: Time-aware QoS prediction for cloud service recommendation based on matrix factorization. IEEE Access. 6, 77716–77724 (2018)

  10. Zhou, Y., Tang, Z., Qi, L., Zhang, X., Dou, W., Wan, S.: Intelligent service recommendation for cold-start problems in edge computing. IEEE Access 7, 46637–46645 (2019)

    Article  Google Scholar 

  11. Li, S., Wen, J., Luo, F., Gao, M., Zeng, J., Dong, Z.Y.: A new qos-aware web service recommendation system based on contextual feature recognition at server-side. IEEE Trans. Netw. Serv. Manag. 1–1, (2017)

  12. Ullah, F., Zhang, B., Khan, R.U., Chung, T.S., Jan, S.: Deep edu: a deep neural collaborative filtering for educational services recommendation. IEEE Access 8, 110915–110928 (2020)

    Article  Google Scholar 

  13. Lian, J., Zhou, X., Zhang, F., Chen, Z., Xie, X., Sun, G.: Xdeepfm: combining explicit and implicit feature interactions for recommender systems. In: 2018 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, London, pp. 1754–1763 (2018)

  14. Gong, J., Zhao, Y., Chen, S., Wang, H., Du, L., Wang, S., Bhuiyan, M., Peng, H., Du, B.: Hybrid deep neural networks for friend recommendations in edge computing environment. IEEE Access 8, 10693–10706 (2020)

    Article  Google Scholar 

  15. Yin, Y., Chen, L., Xu, Y., Jian, W., He, Z., Zhida, M.: QoS prediction for service recommendation with deep feature learning in edge computing environment. Mobile Netw. Appl. 25(2), 391–401 (2020)

    Article  Google Scholar 

  16. Zhong, W., et al.: Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment. Comput. Commun. (2020)

  17. Botangen, K.A., Yu, J., Quan, Z., Han, Y., Yongchareon, S.: Geographic-aware collaborative filtering for web service recommendation. Expert Syst. Appl. 151, 113347 (2020)

    Article  Google Scholar 

  18. Liu, J., Tang, M., Zheng, Z., Liu, X., Lyu, S.: Location-aware and personalized collaborative filtering for web service recommendation. IEEE Trans. Serv. Comput. 9(5), 686–699 (2016)

    Article  Google Scholar 

  19. Ekiz, N., Salih, T., Kucukoner, S., Fidanboylu, K.: An overview of handoff techniques in cellular networks. Int. J. Inf. Technol. 2, 132–136 (2005)

    Google Scholar 

  20. Lianyong, Q., Xuyun, Z., Yiping, W., Yuming, Z.: A social balance theory-based service recommendation approach. In:2015 9th Asia-Pacific Services Computing Conference, Bangkok, Thailand, pp. 48–60 (2015)

  21. Xie, Y., Zhu, Y., Wang, Y., Cheng, Y., Xu, R., Sani, A.S., Yuan, D., Yang, Y.: A novel directional and non-local-convergent particle swarm optimization based workflow scheduling in cloud-edge environment. Future Gener. Comput. Syst. 97, 361–378 (2019)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61702334, 61772200), the Project Supported by Shanghai Natural Science Foundation (Nos. 17ZR1406900, 17ZR1429700) and the Planning Project of Shanghai Institute of Higher Education (No. GJEL18135)

Funding

National Natural Science Foundation of China (Nos. 61702334, 61772200). Shanghai Natural Science Foundation (Nos. 17ZR1406900, 17ZR1429700). The Planning Project of Shanghai Institute of Higher Education (No. GJEL18135)

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China

    Mengshan Yu, Guisheng Fan, Huiqun Yu & Liang Chen

  2. Shanghai Key Laboratory of Computer Software Evaluating and Testing, Shanghai, China

    Mengshan Yu & Liang Chen

Authors
  1. Mengshan Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guisheng Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huiqun Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guisheng Fan.

Ethics declarations

Conflict of interest

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, M., Fan, G., Yu, H. et al. Location-based and Time-aware Service Recommendation in Mobile Edge Computing. Int J Parallel Prog 49, 715–731 (2021). https://doi.org/10.1007/s10766-021-00702-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10766-021-00702-5

Keywords

Search

Navigation