Abstract
In recent years, various methods against service ecosystem have been proposed to address the requirements on recommendation of Web APIs. However, how to effectively combine trivial features of mashups and APIs to improve the recommendation effectiveness remains to be explored. Therefore, we propose a Web API recommendation method using features ensemble and learning-to-rank. Based on available usage data of mashups and Web APIs, textual features, nearest neighbor features, API-specific features, tag features of APIs are extracted to estimate the relevance between the mashup requirement and the candidates of APIs in a regression model, and then a learning-to-rank approach is used to optimize the model. Experimental results show our proposed method is superior to some state-of-the-art methods in the performance of recommendation.
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References
Tan, W., Fan, Y., Ghoneim, A., et al.: From the service-oriented architecture to the web API economy. IEEE Internet Comput. 20(4), 64–68 (2016)
Zhao, H.B. Prashant, D.: Towards automated RESTful web service composition. In: 2009 IEEE International Conference on Web Services, pp. 189–196. IEEE, USA (2009)
Liu, X., Hui, Y., Sun, W., et al.: Towards service composition based on Mashup. In: 2007 IEEE Congress on Services, pp. 332–339. IEEE, USA (2007)
Cao, B., Liu, J., Tang, M., et al.: Mashup service recommendation based on user interest and social network. In: 2013 IEEE 20th International Conference on Services, pp. 99–106. IEEE, USA (2013)
Zhong, Y., Fan, Y., Tan, W., et al.: Web service recommendation with reconstructed profile from mashup descriptions. IEEE Trans. Autom. Sci. Eng. 15(2), 468–478 (2018)
Li, C., Zhang, R., Huai, J., et al.: A novel approach for API recommendation in mashup development. In: 2014 IEEE International Conference on Web Services, pp. 289–296. IEEE, USA (2014)
Cao, B., Liu, X., Rahman, M., et al.: Integrated content and network-based service clustering and web APIs recommendation for mashup development. IEEE Trans. Serv. Comput. 99, 1 (2017)
Bao, Q., Gatlin, P., Maskey, M., et al.: A fine-grained API link prediction approach supporting mashup recommendation. In: 2017 IEEE International Conference on Web Services (ICWS), pp. 220–228. IEEE Computer Society, Honolulu (2017)
Cao, B., Tang, M., Huang, X.: CSCF: a mashup service recommendation approach based on content similarity and collaborative filtering. Int. J. Grid Distrib. Comput. 7(2), 163–172 (2014)
Jiang, Y., Liu, J., Tang, M., et al.: An effective web service recommendation method based on personalized collaborative filtering. In: IEEE International Conference on Web Services, pp. 211–218. IEEE, Washington (2011)
Zheng, Z., Ma, H., Lyu, M.R., et al.: QoS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)
Yao, L., Wang, X., Sheng, Q.Z., et al.: Mashup recommendation by regularizing matrix factorization with API co-invocations. IEEE Trans. Serv. Comput. 99, 1 (2018)
Lo, W., Yin, J., Deng, S., et al.: collaborative web service Qos prediction with location-based regularization. In: 2012 IEEE 19th International Conference on Web Services, pp. 464–471. IEEE Computer Society, Honolulu (2012)
Rahman, M.M., Liu, X., Cao, B.: web API recommendation for mashup development using matrix factorization on integrated content and network-based service clustering. In: 2017 IEEE International Conference on Services Computing (SCC), pp. 225–232. IEEE Computer Society, Honolulu (2017)
Xia, B., Fan, Y., Tan, W., et al.: Category-aware API clustering and distributed recommendation for automatic mashup creation. IEEE Trans. Serv. Comput. 8(5), 674–687 (2017)
Gao, W., Chen, L., Wu, J., et al.: Joint modeling users, services, mashups, and topics for service recommendation. In: 2016 IEEE International Conference on Web Services (ICWS), pp. 260–267. IEEE, San Francisco (2016)
Li, H., Liu, J., Cao, B., et al.: Integrating tag, topic, co-occurrence, and popularity to recommend web APIs for mashup creation. In: IEEE International Conference on Services Computing (SCC), pp. 84–91. IEEE, Honolulu (2017)
Wang, X., Wu, H., Hsu, C.H.: Mashup-oriented API recommendation via random walk on knowledge graph. IEEE Access 7, 7651–7662 (2019)
Li, H.: A short introduction to learning to rank. IEICE Trans. Inf. Syst. E94–D(10), 1854–1862 (2011)
Wu, H., Yue, K., Li, B., et al.: Collaborative QoS prediction with context-sensitive matrix factorization. Future Gener. Comput. Syst. 82, 669–678 (2018)
Huang, K., Fan, Y., Tan, W.: An empirical study of programmable web: a network analysis on a service-mashup system. In: 2012 IEEE 19th International Conference on Web Services (ICWS), pp. 552–559. IEEE, Honolulu (2012)
Wu, H., Pei, Y., Li, B., Kang, Z., Liu, X., Li, H.: Item recommendation in collaborative tagging systems via heuristic data fusion. Knowl.-Based Syst. 75(1), 124–140 (2015)
Hoffman, M.D., Blei, D.M., Bach, F.R.: Online learning for latent dirichlet allocation. In: Advances in Neural Information Processing Systems, vol. 23, pp. 856–864. Natural and Synthetic, Canada (2010)
Robertson, S.E., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. Found. Trends® Inf. Retr. 3(4), 333–389 (2009)
Ketkar, N.: Stochastic gradient descent. In: Deep Learning with Python, pp. 113–132. Apress, Berkeley (2017)
Dojchinovski, M., Vitvar, T., Hoekstra, R.: Linked web apis dataset. Seman. Web 9(3), 1–11 (2017)
Rosen-Zvi, M., Griffiths, T., Steyvers, M., et al.: The author-topic model for authors and documents. In: Proceedings of the 20th Conference in Uncertainty in Artificial Intelligence, pp. 487–494. DBLP, Canada (2004)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (61562090, 61962061), partially supported by the Yunnan Provincial Foundation for Leaders of Disciplines in Science and Technology, the Program for Excellent Young Talents of Yunnan University, the Project of Innovative Research Team of Yunnan Province (2018HC019).
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Zhao, H., Wang, J., Zhou, Q., Wang, X., Wu, H. (2019). Web API Recommendation with Features Ensemble and Learning-to-Rank. In: Jin, H., Lin, X., Cheng, X., Shi, X., Xiao, N., Huang, Y. (eds) Big Data. BigData 2019. Communications in Computer and Information Science, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-1899-7_29
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DOI: https://doi.org/10.1007/978-981-15-1899-7_29
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