Abstract
Quality of Service (QoS) is a non-functional property that reflects the extent to which services provided by the providers meet the needs of the users. As the application of IoT and web services in real world is increasing, QoS prediction is becoming important to predict which service is suitable for a particular user. QoS information is not readily available to providers while providing service recommendation to the users. Thus, there is no certainty in providing the right services to the users. Recommendation systems need better strategies for recommending and managing services according to the user requirements. So, QoS prediction is highly essential for recommending the most suitable service for a user at that instant. In this work, Long short-term memory network (LSTM), Bidirectional long-short term memory network (BiLSTM), convolutional neural network (CNN) and Gated Recurrent Unit Network(GRU) have been used to perform QoS prediction. For this task, data from WS-Dream dataset is used. Initially, Fuzzy C-Means (FCM) algorithm is used to cluster similar users and services. Neural network algorithms have been implemented for accomplishing the prediction task. Prediction is performed in terms of response time and throughput properties. The performance of these algorithms is compared using Mean Absolute Error and Root mean squared error metrices.
A.P. Haripriya and K.S. Vijayanand—These authors contributed equally to this work.
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References
Singh, M., Baranwal, G.: Quality of Service (QoS) in Internet of Things. In: 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU), Bhimtal, India, (2018)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_9
Duong, T.N., Than, V.D., Tran, T.H., Dang, Q.H., Nguyen, D.M., Pham, H.M.: An Effective Similarity Measure for Neighborhood-based Collaborative Filtering. In: 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), IEEE (2019)
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 Transactions on Network and Service Management, vol. 14 (2017)
Agrawal, S.S., Bamnote, G.R.: Implementing and evaluating collaborative filtering (CF) using clustering. In: Satapathy, S.C., Das, S. (eds.) Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 2. SIST, vol. 51, pp. 153–163. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30927-9_16
Verma, V., Aggarwal, R.K.: Neighborhood-based collaborative recommendations: an introduction. In: Johri, P., Verma, J., Paul, S. (eds.) Applications of Machine Learning. Algorithms for Intelligent Systems, Springer, Singapore (2020)
Kant, S., Mahara, T.: Merging user and item based collaborative filtering to alleviate data sparsity. Int. J. Syst. Assur. Eng. Manag. 9, 173–179 (2018)
Das, D., Sahoo, L., Datta, S.: A survey on recommendation system. Int. J Computer Applications, vol. 160 (2017)
Zheng, Z., Ma, H., Lyu, M.R., King, I.: Collaborative web service QoS prediction via neighborhood integrated matrix factorization. IEEE Transactions on Services Computing, vol. 6 (2013)
PradeepKumar, N., Fan, Z.: Hybrid User-Item Based Collaborative Filtering. In: 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, vol. 60, pp. 1453-1461 (2015)
Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook. Springer, Boston, MA (2015)
Gao, H., Xu, Y., Yin, Y., Zhang, W., Li, R., Wang, X.: Context-aware QoS prediction with neural collaborative filtering for internet-of-things services. IEEE Internet Things J. vol. 7 (2020)
Pal, N.R., Pal, K., Keller, J.M., Bezdek, J.C.: A possibilistic fuzzy C-means clustering algorithm. IEEE Trans. Fuzzy Syst. 13(4), 517–530 (2005)
Lenz, I., Knepper, R.A., Saxena, A.: DeepMPC: learning deep latent features for model predictive control. Robotics: Science and Systems (2015)
Lam, X.N., Vu, T., Le, T.D., Duong, A.D.: Addressing cold-start problem in recommendation systems. In: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, pp. 208-211 (2008)
Nasiri, M., Minaei, B., Sharifi, Z.: Adjusting data sparsity problem using linear algebra and machine learning algorithm. Appl. Soft Comput. 61, 1153–1159 (2017)
Zou, G., et al.: DeepTSQP: temporal-aware service QoS prediction via deep neural network and feature integration, Knowledge-Based Systems. vol. 241 (2022)
Kuo, H.-C., Lin, Y.-J.: The optimal estimation of fuzziness parameter in fuzzy c-means algorithm. In: Polkowski, L., et al. (eds.) IJCRS 2017. LNCS (LNAI), vol. 10313, pp. 566–575. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60837-2_45
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Tasneem, A., Haripriya, A., Vijayanand, K. (2023). Context-Aware QoS Prediction for Web Services Using Deep Learning. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_51
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