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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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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