Abstract
The rapid growth in the number of Web APIs, coupled with the myriad of functionally similar Web APIs, makes it difficult to find suitable Web APIs to develop Mashup applications. Even if the existing Web APIs recommendation methods show improvements in service discovery, the accuracy of them can be significantly improved due to overlooking the impact of sparsity and dimension of relationships between Mashup and Web APIs on recommendation accuracy. In this paper, we propose a Web APIs recommendation method for Mashup creation by combining relational topic model and factorization machines technique. This method firstly uses relational topic model to characterize the relationships among Mashup, Web APIs, and their links, and mine the latent topics derived by the relationships. Secondly, it exploits factorization machines to train the latent topics for predicting the link relationship among Mashup and Web APIs to recommend adequate relevant top-k Web APIs for target Mashup creation. 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 precision, recall, and F-measure.
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References
Xia, B., Fan, Y., Tan, W., Huang, K., Zhang, J., Wu, C.: Category-aware API clustering and distributed recommendation for automatic mashup creation. IEEE Trans. Serv. Comput. 8(5), 674–687 (2015)
Liu, L., Lecue, F., Mehandjiev, N.: Semantic content-based recommendation of software services using context. ACM Trans. Web 7(3), 17–20 (2013)
Zheng, Z., Ma, H., Lyu, M., King, I.: Qos-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)
Wu, Q., Iyengar, A., Subramanian, R., Rouvellou, I., Silva-Lepe, I., Mikalsen, T.A.: Combining quality of service and social information for ranking services. In: ICOSC 2009, pp. 561–575 (2009)
Zhong, Y., Fan, Y., Huang, K., Tan, W., Zhang, J.: Time-aware service recommendation for mashup creation in an evolving service ecosystem. In: ICWS 2014, pp. 25–32 (2014)
Yao, L., Sheng, Q.Z., Ngu, H.H., Yu, Y., Segev, A., Yu, J.: Unified collaborative and content-based web service recommendation. IEEE Trans. Serv. Comput. 8(3), 453–466 (2015)
Gao, W., Chen, L., Wu, J., Gao, H.: Manifold-learning based API recommendation for mashup creation. In: ICWS 2015 (2015)
Chen, L., Wang, Y., Yu, Q., Zheng, Z., Wu, J.: WT-LDA: User tagging augmented LDA for web service clustering. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 162–176. Springer, Heidelberg (2013). doi:10.1007/978-3-642-45005-1_12
Liu, X., Fulia, I.: Incorporating user, topic, and service related latent factors into web service recommendation. In: ICWS 2015, pp. 185–192 (2015)
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet Allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Cao, B., Liu, J., Tang, M., Zheng, Z.: Mashup service recommendation based on user interest and social network. In: ICWS 2013, pp. 99–106 (2013)
Chang, J., Blei, D.: Relational topic models for document networks. In: International Conference on Artificial Intelligence and Statistics, pp. 81–88 (2009)
Xu, W., Cao, J., Hu, L., Wang, J., Li, M.: A social-aware service recommendation approach for mashup creation. In: ICWS 2013, pp. 107–114 (2013)
Yao, L., Wang, X., Sheng, Q., Ruan, W., Zhang, W.: Service recommendation for mashup composition with implicit correlation regularization. In: ICWS 2015, pp. 217–224 (2015)
Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 287–296. ACM (2011)
Chen, X., Zheng, Z., Yu, Q., Lyu, M.: Web service recommendation via exploiting location and QoS information. IEEE Trans. Parallel Distrib. Syst. 25(7), 1913–1924 (2014)
Rendle, S.: Factorization machines. In: ICDM 2010, pp. 995–1000 (2010)
Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. (TIST) 3(3), 57–78 (2012)
Li, C., Zhang, R., Huai, J., Guo, X, Sun, H.: A probabilistic approach for web service discovery. In: SCC 2013, pp. 49–56 (2013)
Li, C., Zhang, R., Huai, J., Sun, H.: A novel approach for API recommendation in mashup development. In: ICWS 2014, pp. 289–296 (2014)
Acknowledgments
The work was supported by the National Natural Science Foundation of China under grant No. 61572371, 61572186, 61572187, 61402167, 61402168, State Key Laboratory of Software Engineering (SKLSE) of China (Wuhan University) under grant No. SKLSE2014-10-10.
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Cao, B., Shi, M., Liu, X.(., Liu, J., Tang, M. (2016). Using Relational Topic Model and Factorization Machines to Recommend Web APIs for Mashup Creation. In: Wang, G., Han, Y., MartÃnez Pérez, G. (eds) Advances in Services Computing. APSCC 2016. Lecture Notes in Computer Science(), vol 10065. Springer, Cham. https://doi.org/10.1007/978-3-319-49178-3_30
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DOI: https://doi.org/10.1007/978-3-319-49178-3_30
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