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Web APIs Recommendation for Mashup Development Based on Hierarchical Dirichlet Process and Factorization Machines

  • Buqing CaoEmail author
  • Bing Li
  • Jianxun Liu
  • Mingdong Tang
  • Yizhi Liu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)

Abstract

Mashup technology, which allows software developers to compose existing Web APIs to create new or value-added composite RESTful Web services, has emerged as a promising software development method in a service-oriented environment. More and more service providers have published tremendous Web APIs on the internet, which makes it becoming a significant challenge to discover the most suitable Web APIs to construct user-desired Mashup application from these tremendous Web APIs. In this paper, we combine hierarchical dirichlet process and factorization machines to recommend Web APIs for Mashup development. This method, firstly use the hierarchical dirichlet process to derive the latent topics from the description document of Mashups and Web APIs. Then, it apply factorization machines train the topics obtained by the HDP for predicting the probability of Web APIs invocated by Mashups and recommending the high-quality Web APIs for Mashup development. Finally, we conduct a comprehensive evaluation to measure performance of our method. Compared with other existing recommendation approaches, experimental results show that our approach achieves a significant improvement in terms of MAE and RMSE.

Keywords

Hierarchical dirichlet process Factorization machines Web APIs recommendation Mashup development 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China under grant No. 61572371, 61572186, 61572187, 61402167, 61402168, State Key Laboratory of Software Engineering of China (Wuhan University) under grant No.SKLSE2014-10-10, Open Foundation of State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) under grant No. SKLNST-2016-2-26, Hunan Provincial Natural Science Foundation of China under grant No. 2015JJ2056,2017JJ2098,Hunan Provincial University Innovation Platform Open Fund Project of China under grant No.14K037, Education Science Planning Project of Hunan Province under grant No. XJK013CGD009, and Language Application Research Project of Hunan Province under grant No. XYJ2015GB09.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Buqing Cao
    • 1
    • 2
    Email author
  • Bing Li
    • 2
  • Jianxun Liu
    • 1
  • Mingdong Tang
    • 1
  • Yizhi Liu
    • 1
  1. 1.School of Computer Science and EngineeringHunan University of Science and TechnologyXiangtanChina
  2. 2.State Key Laboratory of Software Engineering, International School of SoftwareWuhan UniversityWuhanChina

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