Abstract
With the exponential increase in Web Application Programming Interfaces (APIs), selecting appropriate APIs to construct a mashup is a challenging task. When multiple APIs are put together, their overall function is not just a superposition of their individual functions in many cases. Unfortunately, the approaches proposed to date do not sufficiently model the synthetical functions of the combined APIs. In this paper, an API Package recommender system based on composition feature learning (API-Prefer) is proposed. API-Prefer tries to learn the composition features of an API pair. Then the composition features can be used to predict whether this API pair can be adopted by a mashup or not. Specifically, a deep neural network is designed for composition feature learning and adoption probability prediction in API-Prefer. Since there is a large amount of API pairs, API-Prefer applies a strategy to select the potential APIs first, then the API packages can be discovered based on the predicted scores over multiple API pairs. Experiments on a real-world dataset show API-Prefer is significantly better than the comparative methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Platzer, C., Dustdar, S.: A vector space search engine for web services. In: Third European Conference on Web Services (ECOWS 2005), pp. 9-pp. IEEE (2005)
Fletcher, K.: Regularizing matrix factorization with implicit user preference embeddings for web API recommendation. In: 2019 IEEE International Conference on Services Computing (SCC), pp. 1–8. IEEE (2019)
Maaradji, A., Hacid, H., Skraba, R., Vakali, A.: Social web mashups full completion via frequent sequence mining. In: 2011 IEEE World Congress on Services, pp. 9–16. IEEE (2011)
Cao, J., Lu, Y., Zhu, N.: Service package recommendation for mashup development based on a multi-level relational network. In: Sheng, Q.Z., Stroulia, E., Tata, S., Bhiri, S. (eds.) ICSOC 2016. LNCS, vol. 9936, pp. 666–674. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46295-0_46
Li, C., Zhang, R., Huai, J., Sun, H.: A novel approach for API recommendation in mashup development. In: 2014 IEEE International Conference on Web Services, pp. 289–296. IEEE (2014)
Jain, A., Liu, X., Yu, Q.: Aggregating functionality, use history, and popularity of APIs to recommend mashup creation. In: Barros, A., Grigori, D., Narendra, N.C., Dam, H.K. (eds.) ICSOC 2015. LNCS, vol. 9435, pp. 188–202. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48616-0_12
Xu, W., Cao, J., Hu, L., Wang, J., Li, M.: A social-aware service recommendation approach for mashup creation. In: 2013 IEEE 20th International Conference on Web Services, pp. 107–114. IEEE (2013)
Acknowledgement
This work is supported by National Key Research and Development Plan (No. 2018YFB1003800).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, Y., Cao, J. (2020). API-Prefer: An API Package Recommender System Based on Composition Feature Learning. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds) Service-Oriented Computing. ICSOC 2020. Lecture Notes in Computer Science(), vol 12571. Springer, Cham. https://doi.org/10.1007/978-3-030-65310-1_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-65310-1_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65309-5
Online ISBN: 978-3-030-65310-1
eBook Packages: Computer ScienceComputer Science (R0)