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QoS Prediction for Service Recommendation with Deep Feature Learning in Edge Computing Environment

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Abstract

Along with the popularity of intelligent services and mobile services, service recommendation has become a key task, especially the task based on quality-of-service (QoS) in edge computing environment. Most existing service recommendation methods have some serious defects, and cannot be directly adopted in edge computing environment. For example, most of existing methods cannot learn deep features of users or services, but in edge computing environment, there are a variety of devices with different configurations and different functions, and it is necessary to learn deep features behind those complex devices. In order to fully utilize hidden features, this paper proposes a new matrix factorization (MF) model with deep features learning, which integrates a convolutional neural network (CNN). The proposed mode is named Joint CNN-MF (JCM). JCM is capable of using the learned deep latent features of neighbors to infer the features of a user or a service. Meanwhile, to improve the accuracy of neighbors selection, the proposed model contains a novel similarity computation method. CNN learns the neighbors features, forms a feature matrix and infers the features of the target user or target service. We conducted experiments on a real-world service dataset under a batch of cases of data densities, to reflect the complex invocation cases in edge computing environment. The experimental results verify that compared to counterpart methods, our method can consistently achieve higher QoS prediction results.

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References

  1. Deng S, Xiang Z, Yin J, Taheri J, Albert Y (2018) Zomaya: Composition-Driven IoT Service Provisioning in Distributed Edges. IEEE Access 6:54258–54269

    Article  Google Scholar 

  2. Xia Y, Zhou MC, Luo X, Zhu Q, Li J, Huang Y (2015) Stochastic Modeling and Quality Evaluation of Infrastructure-as-a-Service Clouds. IEEE Trans Automation Science and Engineering 12(1):162–170

    Article  Google Scholar 

  3. Zhang C, Zhao H, Deng S (2018) A Density-Based Offloading Strategy for IoT Devices in Edge Computing Systems. IEEE Access 6:73520–73530

    Article  Google Scholar 

  4. Peng Q, Zhou M, He Q, Xia Y, Wu C, Deng S (2018) Multi-Objective Optimization for Location Prediction of Mobile Devices in Sensor-Based Applications. IEEE Access 6:77123–77132

    Article  Google Scholar 

  5. Park Y, Park S, Jung W (2015) Reversed CF: A fast collaborative filtering algorithm using a k-nearest neighbor graph. Expert Syst Appl 42(8):4022–4028

    Article  Google Scholar 

  6. Zheng Z, Ma H, Lyu MR (2010) QoS-Aware Web Service Recommendation by Collaborative Filtering. IEEE Trans Services Computing 4(2):140–152

    Article  Google Scholar 

  7. Sharifi Z, Rezghi M, Nasiri M (2014) A new algorithm for solving data sparsity problem based-on Non-negative matrix factorization in recommender systems. Int. Conf. Computer and Knowledge Engineering, pp. 56–61

  8. Chen X, Zheng Z, Yu Q (2014) Web Service Recommendation via Exploiting Location and QoS Information. IEEE Trans Parallel and Distributed Syst 25(7):1913–1924

    Article  Google Scholar 

  9. Koren Y, Bell R, Volinsky C (2009) Matrix Factorization Techniques for Recommender Systems. Computer. 42(8):30–37

    Article  Google Scholar 

  10. Kai SU, Liang-Li MA, Sun YF et al (2015) Non-negative matrix factorization model for Web service QoS prediction. J Zhejiang University 49(7):1358–1366

    Google Scholar 

  11. Zhang S, Yao L, Sun A (2017) Deep learning based recommender system: A survey and new perspectives. In CoRR, abs/1707.07435

  12. Gao H, Mao S, Huang W, Yang X (2018) Applying Probabilistic Model Checking to Financial Production Risk Evaluation and Control: A Case Study of Alibaba's Yu'e Bao. IEEE Transactions on Computational Social Systems (TCSS) 5(3):785–795

    Article  Google Scholar 

  13. Yu F, Zhang Y, Song S (2015) LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. Computer Science

  14. Yu J, Kuang Z, Zhang B, Zhang W, Lin D, Fan J (2018) Leveraging Content Sensitiveness and User Trustworthiness to Recommend Fine-Grained Privacy Settings for Social Image Sharing. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/TIFS.2017.2787986

  15. Yu J, Hong C, Rui Y, Tao D (2018) Multitask Autoencoder Model for Recovering Human Poses. IEEE Trans Ind Electron 65(6):5060–5068

    Article  Google Scholar 

  16. Kim D, Park C, Oh J (2016) Convolutional matrix factorization for document context-aware recommendation. Int Conf Recommender Systems:233–240

  17. Chen L, Ha W (2018) Reliability prediction and QoS selection for web service composition. Int J Comput Sci Eng 16(2):202

    Google Scholar 

  18. Wang S, Zhao Y, Huang L (2017) QoS prediction for service recommendations in mobile edge computing. J. Parallel and Distributed Computing

  19. Yin Y, Song A, Min G (2016) QoS Prediction for Web Service Recommendation with Network Location-Aware Neighbor Selection. Int J Softw Eng Knowl Eng 26(4):611–632

    Article  Google Scholar 

  20. Zheng Z, Chen J, Lyu MR (2013) Personalized Web Service Recommendation via Normal Recovery Collaborative Filtering. IEEE Trans Services Computing 6(4):573–579

    Article  Google Scholar 

  21. Yao L, Sheng QZ, Segev A (2013) Recommending web services via combining collaborative filtering with content-based features. Int Conf Web Services:42–49

  22. Xin M, Zhang Y, Li S (2017) A Location-Context Awareness Mobile Services Collaborative Recommendation Algorithm Based on User Behavior Prediction. International Journal of Web Services Research 14(2):45–66

    Article  Google Scholar 

  23. Ren L, Wang W (2017) An SVM-based collaborative filtering approach for Top-N web services recommendation. Future Generation Computer Systems

  24. Chen Z, Shen L, Li F (2017) Your neighbors alleviate cold-start: On geographical neighborhood influence to collaborative web service QoS prediction. Knowl-Based Syst 138:188–201

    Article  Google Scholar 

  25. Xu Y, Yin J, Wei L (2013) Personalized Location-Aware QoS Prediction for Web Services Using Probabilistic Matrix Factorization. Int Conf. Web Information Systems Engineering, pp. 229–242

  26. He P, Zhu J, Zheng Z (2014) Location-based hierarchical matrix factorization for web service recommendation. Int Conf. on Web Services, pp. 297–304

  27. Li S, Wen J, Luo F (2017) A New QoS-Aware Web Service Recommendation System based on Contextual Feature Recognition at Server-Side. IEEE Trans Network and Service Management PP(99):1–1

    Google Scholar 

  28. Zhou L, Song Z, Zhai S (2014) Predicting Web Service QoS via Combining Matrix Factorization with Network Location. International Journal of U and E-Service Science and Technology 7(3):303–317

    Article  Google Scholar 

  29. Zhou C, Zhang W, Li B (2014) Web Service Recommendation via Exploiting Temporal QoS Information. In Algorithms and Architectures for Parallel Processing, pp. 15–27

  30. Wu C, Qiu W, Zheng Z (2015) QoS prediction of web services based on two-phase K-means clustering. Int Conf. Web Services,, pp. 161–168

  31. Tian G, Wang J, He K (2017) Integrating implicit feedbacks for time-aware web service recommendations. Inf Syst Front 19(1):1–15

    Article  Google Scholar 

  32. Tuan T X, Tu MP (2017) 3D Convolutional networks for session-based recommendation with content features. Int Conf. Recommender Systems, pp. 138–146

  33. Xing S, Liu F, Zhao X (2017) Points-of-interest recommendation based on convolution matrix factorization. Appl Intell C:1–12

    Google Scholar 

  34. Huan H, Wei Z, Liang L (2017) Collaborative filtering recommendation model based on convolutional denoising auto encoder. In Chinese Conference., pp. 64–71

  35. Zheng Z, Ma H, Lyu MR (2009) WSRec: A Collaborative Filtering Based Web Service Recommender System. Int. Conf. Web Services, pp. 437–444

  36. Resnick P, Iacovou N, Suchak M (1994) GroupLens: an open architecture for collaborative filtering of netnews. In ACM Conference on Computer Supported Cooperative Work, pp. 175–186

  37. Sarwar B, Karypis G, Konstan J (2001) Item-based collaborative filtering recommendation algorithms. Int Conf. World Wide Web, pp. 285–295

  38. Yin J, Xu Y (2015) Personalised QoS-based web service recommendation with service neighborhood-enhanced matrix factorization. Int J Web and Grid Services 11(1):39–56

    Article  Google Scholar 

  39. Xu Y, Yin J, Deng S (2016) Context-aware QoS prediction for web service recommendation and selection. Expert Syst Appl 53:75–86

    Article  Google Scholar 

  40. Li S, Wen J, Luo F, Tian C, Xiong Q (2017). A location and reputation aware matrix factorization approach for personalized quality of service prediction. IEEE International Conference on Web Services, pp. 652–659

Download references

Acknowledgements

This paper is supported by the National Key Research and Development Program of China (No.2017YFB1400601), National Natural Science Foundation of China (No. 61872119, No. 61702391), Natural Science Foundation of Zhejiang Province (No. LY16F020017) and Shaanxi Province (No.2018JQ6050), and Fundamental Research Funds for Central Universities (JBX171007).

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Authors and Affiliations

  1. School of Computer, Hangzhou Dianzi University, Hangzhou, 310018, China

    Yuyu Yin & Lu Chen

  2. Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education, Hangzhou, 310018, China

    Yuyu Yin & Jian Wan

  3. School of Computer Science and Technology, Xidian University, Xi’an, 710126, China

    Yueshen Xu & He Zhang

  4. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, Jiangsu, China

    Yueshen Xu

  5. School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China

    Jian Wan

  6. Xanten Guangdong Development Co., Ltd, Foshan, 528200, China

    Zhida Mai

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  1. Yuyu Yin
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  2. Lu Chen
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Correspondence to Yueshen Xu.

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Yin, Y., Chen, L., Xu, Y. et al. QoS Prediction for Service Recommendation with Deep Feature Learning in Edge Computing Environment. Mobile Netw Appl 25, 391–401 (2020). https://doi.org/10.1007/s11036-019-01241-7

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