Abstract
With the prevalence of Web/Cloud/IoT services on the Internet, to select service with high quality is of paramount importance for building reliable distributed applications. However, the accurate values of the quality of services (QoS) are usually uneasy to obtain for they are typically personalized and highly depend on the contexts of users and services such as locations, bandwidths and other network conditions. Therefore, personalized and context-aware QoS prediction methods are desirable. By exploiting the QoS records generated by a set of users on a set of services, this paper proposes a collaborative QoS prediction method based on Context-Aware Factorization Machines named CAFM, which integrates the context information of services and users with classic factorization machines. Comprehensive experiments conducted on a real-world dataset show that the proposed method significantly outperforms existing methods in prediction accuracy.
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References
Chen, X., Liang, W., Xu, J., Wang, C., Li, K. C., Qiu, M.: An efficient service recommendation algorithm for cyber-physical-social systems. In: IEEE Transactions on Network Science and Engineering (2021). https://doi.org/10.1109/TNSE.2021.3092204
Liang, W., Ning, Z., Xie, S., Hu, Y., Lu, S., Zhang, D.: Secure fusion approach for the internet of things in smart autonomous multi-robot systems. Inform. Sci. 1–20 (2021). https://doi.org/10.1016/j.ins.2021.08.035
Tang, M., Xia, Y., Tang, B., Zhou, Y., Cao, B., Hu, R.: Mining collaboration patterns between APIs for mashup creation in web of things. In: IEEE Access, vol. 7, pp. 14206–14215 (2019)
Gao, H., Qin, X., Barroso, R.J.D., Hussain, W., Xu, Y., Yin, Y.: Collaborative learning-based industrial IoT API recommendation for software-defined devices: the implicit knowledge discovery perspective. In: IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1–11 (2020)
Tang, M., Zheng, Z., Kang, G., Liu, J., Yang, Y., Zhang, T.: Collaborative web service quality prediction via exploiting matrix factorization and network map. IEEE Trans. Netw. Serv. Manage. 13(1), 126–137 (2016)
Liu, J., Tang, M., Zheng, M., Liu, X., Lyu, S.: Location-aware and personalized collaborative filtering for web service recommendation. IEEE Trans. Serv. Comput. 9(5), 686–699 (2016)
Zheng, Z., Li, X., Tang, M., Xie, F., Lyu, M.R.: Web service QoS prediction via collaborative filtering: a survey. IEEE Trans. Serv. Comput. (2020)
Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining, IEEE. pp. 995–1000 (2010)
Rendle, S., Gantner, Z., Freudenthaler, C., Schmidt-Thieme, L.: Fast context-aware recommendations with factorization machines, In: Proc. 34th Int. ACM SIGIR Conf. Res. Develop. Inf. Retr., Beijing, China, Jul. 2011, pp. 635–644 (2011)
Zheng, Z., Zhang, Y., Lyu, M.R.: Investigating QoS of real-world web services. IEEE Trans. Serv. Comput. 7(1), 32–39 (2014)
Shao, L., Zhang, J., Wei, Y., Zhao, J., Xie, B., Mei, M.: Personalized QoS prediction for web services via collaborative filtering. In: The 14th IEEE International Conference on Web Services (ICWS 2007), pp.439–446 (2007)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: The 10th International Conference on World Wide Web (WWW 2001), pp.285–295 (2001)
Zheng, Z., Ma, H., Lyu, M.R., King., I.: WSRec: A collaborative filtering based web service recommender system. In: IEEE International Conference on Web Services, pp. 437–444 (2009)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: The Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007), pp.1257–1264 (2007)
Yang, Y., Zheng, Z., Niu, X., Tang, M., Lu, Y., Liao, X.: A location-based factorization machine model for web service QoS prediction. IEEE Trans. Serv. Comput. 14(5), 1264–1277 (2021)
Tang, M., Jiang, Y., Liu, J., Liu, M.: Location-aware collaborative filtering for QoS-based service recommendation. In: 2012 IEEE 19th International Conference on Web Services (ICWS 2012), pp. 202–209 (2012)
Tang, M., Zhang, T., Liu, J., Chen, J.: Cloud service QoS prediction via exploiting collaborative filtering and location-based data smoothing. Concurr. Comput. Pract. Exp. 27(18), 5826–5839 (2015)
Ma, Y., Wang, S., Hung, P.C.K., Hsu, C., Sun, Q., Yang, F.: A highly accurate prediction algorithm for unknown web service QoS values. IEEE Trans. Serv. Comput. 9(4), 511–523 (2016)
Zhang, Y., Zheng, Z., Lyu, M.R.: Exploring latent features for memory-based QoS prediction in cloud computing. In: 2009 IEEE International Conference on Web Services, pp. 437–444 (2009)
Zheng, Z., Ma, H., Lyu, M.R., King, I.: Collaborative web service QoS prediction via neighborhood integrated matrix factorization. IEEE Trans. Serv. Comput. 6(3), 289–299 (2013)
Chen, Z., Sun, Y., You, D., Li, F., Shen, L.: An accurate and efficient web service QoS prediction model with wide-range awareness. Future Gener. Comput. Syst. 109, 275–292 (2020)
Acknowledgement
This work is supported by National Natural Science Foundation of China under grant no. 61976061 and the Opening Project of Guangdong Key Laboratory of Big Data Analysis and Processing (202003).
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Tang, W., Tang, M., Liang, W. (2022). Collaborative QoS Prediction via Context-Aware Factorization Machine. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13157. Springer, Cham. https://doi.org/10.1007/978-3-030-95391-1_12
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