Abstract
The rapid increase in the number and diversity of web APIs with similar functionality, makes it challenging to find suitable ones for mashup development. In order to reduce the number of similarly functional web APIs, recommender systems are used. Various web API recommendation methods exist which attempt to improve recommendation accuracy, by mainly using some discovered relationships between web APIs and mashups. Such methods are basically incapable of recommending quality web APIs because they fail to incorporate web API quality in their recommender systems. In this work, we propose a method that considers the quality features of web APIs, to make quality web API recommendations. Our proposed method uses web API quality to estimate their relevance for recommendation. Specifically, we propose a matrix factorization method, with quality feature regularization, to make quality web API recommendations and also enhance recommendation diversity. We demonstrate the effectiveness of our method by conducting experiments on a real-world dataset from www.programmableweb.com. Our results not only show quality web API recommendations, but also, improved recommendation accuracy. In addition, our proposed method improves recommendation diversity by mitigating the negative Matthew effect of accumulated advantage, intrinsic to most existing web API recommender systems. We also compare our method with some baseline recommendation methods for validation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fletcher, K.K.: A quality-based web API selection for mashup development using affinity propagation. In: Ferreira, J.E., Spanoudakis, G., Ma, Y., Zhang, L.-J. (eds.) SCC 2018. LNCS, vol. 10969, pp. 153–165. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94376-3_10
Yao, L., Wang, X., Sheng, Q.Z., Benatallah, B., Huang, C.: Mashup recommendation by regularizing matrix factorization with API co-invocations. IEEE Trans. Serv. Comput. (2018)
Santos, W.: Research shows interest in providing APIs still high. https://www.programmableweb.com/news/research-shows-interest-providing-apis-still-high/research/2018/02/23. Accessed 18 Oct 2018
Zhong, Y., Fan, Y., Tan, W., Zhang, J.: Web service recommendation with reconstructed profile from mashup descriptions. IEEE Trans. Autom. Sci. Eng. 15(2), 468–478 (2018)
Cao, B., Liu, X., Rahman, M.M., Li, B., Liu, J., Tang, M.: Integrated content and network-based service clustering and web APIs recommendation for mashup development. IEEE Trans. Serv. Comput. (2017)
Rahman, M.M., Liu, X., Cao, B.: Web API recommendation for mashup development using matrix factorization on integrated content and network-based service clustering. In: IEEE International Conference on Services Computing (SCC), pp. 225–232. IEEE (2017)
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)
Buqing, C., Tang, M., Huang, X.: CSCF: a mashup service recommendation approach based on content similarity and collaborative filtering. Int. J. Grid Distrib. Comput. 7(2), 163–172 (2014)
Li, H., Liu, J., Cao, B., Tang, M., Liu, X., Li, B.: Integrating tag, topic, co-occurrence, and popularity to recommend web APIs for mashup creation. In: IEEE International Conference on Services Computing (SCC), pp. 84–91. IEEE (2017)
Fletcher, K.K.: A method for dealing with data sparsity and cold-start limitations in service recommendation using personalized preferences. In: IEEE International Conference on Cognitive Computing (ICCC), pp. 72–79, June 2017
Gu, Q., Cao, J., Peng, Q.: Service package recommendation for mashup creation via mashup textual description mining. In: IEEE International Conference on Web Services (ICWS), pp. 452–459, June 2016
Fletcher, K.K., Liu, X.F.: A collaborative filtering method for personalized preference-based service recommendation. In: IEEE International Conference on Web Services, pp. 400–407, June 2015
Rigney, D.: The Matthew Effect: How Advantage Begets Further Advantage. Columbia University Press, New York (2010)
Cappiello, C., Daniel, F., Matera, M.: A quality model for mashup components. In: Gaedke, M., Grossniklaus, M., Díaz, O. (eds.) ICWE 2009. LNCS, vol. 5648, pp. 236–250. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02818-2_19
Tran, T., Lee, K., Liao, Y., Lee, D.: Regularizing matrix factorization with user and item embeddings for recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, pp. 687–696. ACM, New York (2018)
Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Hierarchical Dirichlet processes. J. Am. Stat. Assoc. 101(476), 1566–1581 (2006)
Fuglede, B., Topsoe, F.: Jensen-Shannon divergence and Hilbert space embedding. In: Proceedings of International Symposium on Information Theory, ISIT 2004, June 2004
Fletcher, K.: A method for aggregating ranked services for personal preference based selection. Int. J. Web Serv. Res. (IJWSR) 16(2), 1–23 (2019)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, pp. 263–272, December 2008
Tejeda-Lorente, Á., Porcel, C., Peis, E., Sanz, R., Herrera-Viedma, E.: A quality based recommender system to disseminate information in a university digital library. Inf. Sci. 261, 52–69 (2014)
Gao, W., Chen, L., Wu, J., Gao, H.: Manifold-learning based API recommendation for mashup creation. In: IEEE International Conference on Web Services (ICWS), pp. 432–439. IEEE (2015)
Li, C., Zhang, R., Huai, J., Sun, H.: A novel approach for API recommendation in mashup development. In: IEEE International Conference on Web Services (ICWS), pp. 289–296. IEEE (2014)
Xue, Q., Liu, L., Chen, W., Chuah, M.C.: Automatic generation and recommendation for API mashups. In: 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 119–124. IEEE (2017)
Cao, B., et al.: Mashup service clustering based on an integration of service content and network via exploiting a two-level topic model. In: IEEE International Conference on Web Services (ICWS), pp. 212–219. IEEE (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Fletcher, K.K. (2019). A Quality-Aware Web API Recommender System for Mashup Development. In: Ferreira, J., Musaev, A., Zhang, LJ. (eds) Services Computing – SCC 2019. SCC 2019. Lecture Notes in Computer Science(), vol 11515. Springer, Cham. https://doi.org/10.1007/978-3-030-23554-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-23554-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23553-6
Online ISBN: 978-3-030-23554-3
eBook Packages: Computer ScienceComputer Science (R0)