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.
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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)
https://en.wikipedia.org/wiki/Mashup_(web_application_hybrid)
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)
The, Y., Jordan, M., Beal, M., Blei, D.: Hierarchical dirichlet process. J. Am. Stat. Assoc. 101(476), 1566–1581 (2004)
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)
Ma, T., Sato, I., Nakagawa, H.: The hybrid nested/hierarchical dirichlet process and its application to topic modeling with word differentiation. In: AAAI 2015 (2015)
Teh, Y., Jordan, M., Beal, M., Blei, D.: Sharing clusters among related groups: hierarchical dirichlet processes. Adv. Neural Inf. Process. Syst. 37(2), 1385–1392 (2004)
Zheng, Z., Ma, H., Lyu, M., King, I.: WSRec: a collaborative filtering based web service recommender system. In: ICWS 2009, Los Angeles, CA, USA, 6–10 July, 2009, pp. 437–444 (2009)
Picozzi, M., Rodolfi, M., Cappiello, C., Matera, M.: Quality-based recommendations for mashup composition. In: Daniel, F., Facca, F.M. (eds.) ICWE 2010. LNCS, vol. 6385, pp. 360–371. Springer, Heidelberg (2010). doi:10.1007/978-3-642-16985-4_32
Cappiello, C., Daniel, F., Matera, M.: A quality model for mashup components. In: Gaedke, M., Grossniklaus, M., DÃaz, O. (eds.) ICWE 2009. LNCS, vol. 5648, pp. 236–250. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02818-2_19
Cappiello, C., Daniel, F., Matera, M., Pautasso, C.: Information quality in mashups. IEEE Internet Comput. 14(4), 14–22 (2010)
Huang, K., Fan, Y., Tan, W.: An empirical study of programmable web: a network analysis on a service-mashup system. In: ICWS 2012, 24–29 June, Honolulu, Hawaii, USA (2012)
Gao, W., Chen, L., Wu, J., Gao. H.: Manifold-learning based API recommendation for mashup creation. In: ICWS 2015, June 27 - July 2, New York, USA (2015)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Zheng, Z., Ma, H., Lyu, M.R., King, I.: Collaborative web service QoS prediction via neighborhood integrated matrix factorization. IEEE Trans. Serv. Comput. 6(3), 289–299 (2013)
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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Cao, B., Li, B., Liu, J., Tang, M., Liu, Y. (2017). Web APIs Recommendation for Mashup Development Based on Hierarchical Dirichlet Process and Factorization Machines. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_1
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