API recommendation for Mashup creation based on neural graph collaborative filtering

References

• Berg, R. v. d., Kipf, T. N., & Welling, M. (2017). Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263.
   Google Scholar
• Cao, B., Liu, X. F., Liu, J., & Tang, M. (2017). Domain-aware Mashup service clustering based on LDA topic model from multiple data sources. Information and Software Technology, 90, 40–54. https://doi.org/10.1016/j.infsof.2017.05.001
   Web of Science ®Google Scholar
• Fletcher, K. (2019). Regularizing matrix factorization with implicit user preference embeddings for Web API recommendation. 2019 IEEE International Conference on Services Computing (SCC).
   Google Scholar
• Gu, Q., Cao, J., & Peng, Q. (2016). Service package Recommendation for Mashup Creation via Mashup Textual description mining. 2016 IEEE International Conference on Web Services (ICWS).
   Google Scholar
• Hao, Y., Fan, Y., Tan, W., & Zhang, J. (2017). Service recommendation based on targeted reconstruction of Service descriptions. 2017 IEEE International Conference on Web Services (ICWS).
   Google Scholar
• He, Q., Zhou, R., Zhang, X., Wang, Y., Ye, D., Chen, F., Grundy, J. C., & Yang, Y. (2017a). Keyword search for building service-based systems. IEEE Transactions on Software Engineering, 43(7), 658–674. https://doi.org/10.1109/tse.2016.2624293
   Web of Science ®Google Scholar
• He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017b). Neural collaborative filtering. Proceedings of the 26th international conference on world wide web.
   Google Scholar
• Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
   Google Scholar
• Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
   Google Scholar
• Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. https://doi.org/10.1109/MC.2009.263
   Web of Science ®Google Scholar
• Li, C., Zhang, R., Huai, J., Guo, X., & Sun, H. (2013). A probabilistic approach for web service discovery. 2013 IEEE International Conference on Services computing.
   Google Scholar
• Liang, T., Chen, L., Wu, J., Dong, H., & Bouguettaya, A. (2016). Meta-path based service recommendation in heterogeneous information networks. International Conference on service-oriented computing.
   Google Scholar
• Liang, W., Li, Y., Xu, J., Zheng, Q., Zhang, D., & Li, K.-C. (2021a). Accurate and robust QoS prediction for large-scale distributed services via neural network. IEEE Transactions on Computers.
   PubMedGoogle Scholar
• Liang, W., Tang, M., Long, J., Peng, X., Xu, J., & Li, K.-C. (2019). A secure fabric blockchain-based data transmission technique for industrial internet-of-things. IEEE Transactions on Industrial Informatics, 15(6), 3582–3592. https://doi.org/10.1109/TII.2019.2907092
   Web of Science ®Google Scholar
• Liang, W., Xie, S., Cai, J., Xu, J., Hu, Y., Xu, Y., & Qiu, M. (2021b). Deep neural network security collaborative filtering scheme for Service recommendation in intelligent cyber-physical systems. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3086845
   Google Scholar
• Liu, J., Tang, M., Zheng, Z., Liu, X., & Lyu, S. (2016). Location-aware and personalized collaborative filtering for web service recommendation. IEEE Transactions on Services Computing, 9(5), 686–699. https://doi.org/10.1109/tsc.2015.2433251
   Web of Science ®Google Scholar
• Ma, Y., Geng, X., & Wang, J. (2020). A deep neural network with multiplex interactions for cold-start service recommendation. IEEE Transactions on Engineering Management, 68, 1, 105–119. https://doi.org/10.1109/tem.2019.2961376
   Web of Science ®Google Scholar
• Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012). BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618.
   Google Scholar
• Tang, B., Tang, M., Xia, Y., & Hsieh, M.-Y. (2021). Composition pattern-aware web service recommendation based on depth factorisation machine. Connection Science, 1–21. https://doi.org/10.1080/09540091.2021.1911933
   Google Scholar
• Tang, M., Xia, Y., Tang, B., Zhou, Y., Cao, B., & Hu, R. (2019a). Mining collaboration patterns between APIs for Mashup creation in web of things. IEEE Access, 7, 14206–14215. https://doi.org/10.1109/access.2019.2894297
   Web of Science ®Google Scholar
• Tang, M., Xie, F., Liang, W., Xia, Y., & Li, K.-C. (2019b). Predicting new composition relations between web services via link analysis. International Journal of Computational Science and Engineering, 20(1), 88–101. https://doi.org/10.1504/IJCSE.2019.103256
   Web of Science ®Google Scholar
• Tang, M., Zheng, Z., Kang, G., Liu, J., Yang, Y., & Zhang, T. (2016). Collaborative web service quality prediction via exploiting matrix factorization and network map. IEEE Transactions on Network and Service Management, 13(1), 126–137. https://doi.org/10.1109/TNSM.2016.2517097
   Web of Science ®Google Scholar
• Wang, H., Wang, N., & Yeung, D.-Y. (2015). Collaborative deep learning for recommender systems. Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining.
   Google Scholar
• Wang, X., He, X., Wang, M., Feng, F., & Chua, T.-S. (2019). Neural graph collaborative filtering. Proceedings of the 42nd International ACM SIGIR Conference on research and development in information retrieval.
   Google Scholar
• Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., & Jegelka, S. (2018). Representation learning on graphs with jumping knowledge networks. International Conference on machine learning.
   Google Scholar
• Xu, W., Cao, J., Hu, L., Wang, J., & Li, M. (2013). A social-aware Service recommendation Approach for Mashup creation. 2013 IEEE 20th International Conference on Web Services.
   Google Scholar
• Yang, J.-H., Chen, C.-M., Wang, C.-J., & Tsai, M.-F. (2018). HOP-rec: high-order proximity for implicit recommendation. Proceedings of the 12th ACM Conference on recommender systems.
   Google Scholar
• Yao, L., Wang, X., Sheng, Q. Z., Ruan, W., & Zhang, W. (2015). Service recommendation for Mashup composition with implicit correlation regularization. 2015 IEEE International Conference on Web Services.
   Google Scholar
• Zang, N., & Rosson, M. B. (2008). What’s in a mashup? And why? Studying the perceptions of web-active end users. 2008 IEEE symposium on visual languages and human-centric computing.
   Google Scholar
• Zhang, N., Wang, J., & Ma, Y. (2017a). Mining Domain knowledge on Service goals from Textual Service descriptions. IEEE Transactions on Services Computing, 13, 1–1. https://doi.org/10.1109/tsc.2017.2693147
   Google Scholar
• Zhang, N., Wang, J., & Ma, Y. (2017b). Mining domain knowledge on service goals from textual service descriptions. IEEE Transactions on Services Computing, 13(3), 488–502. https://doi.org/10.1109/TSC.2017.2693147
   Google Scholar
• Zhang, Y., Wang, K., He, Q., Chen, F., Deng, S., Zheng, Z., & Yang, Y. (2019a). Covering-based web service quality prediction via neighborhood-aware matrix factorization. IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2019.2891517
   Google Scholar
• Zhang, Y., Yin, C., Wu, Q., He, Q., & Zhu, H. (2019b). Location-aware deep collaborative filtering for service recommendation. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/TSMC.2019.2931723
   Google Scholar
• Zheng, Z., Xiaoli, L., Tang, M., Xie, F., & Lyu, M. R. (2020). Web Service QoS Prediction via collaborative filtering: A survey. IEEE Transactions on Services Computing, 1–1. https://doi.org/10.1109/tsc.2020.2995571
   Google Scholar