Abstract
In this paper, we propose a novel approach to recommend services for a given mashup development task. We model service data as a heterogeneous service graph which includes multiple types of nodes and edges to capture rich information extracted from the data. We extend the design of the graph convolutional networks to learn optimal graph embeddings based on a novel structure alignment framework leveraging the latent heterogeneous graph structural features. We then design a ranking mechanism to recommend those services so that their links to the mashup can best fit the latent graph structural features. Both the embedding learning and ranking process make the use of meta-paths to incorporate prior domain knowledge into recommendation. A comprehensive experimental study is conducted on a real-world data set and the result indicates that our approach can significantly outperform the existing solutions.
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Gao, Z., et al.: SeCo-LDA: mining service co-occurrence topics for recommendation. In: IEEE International Conference on Web Services, pp. 25–32 (2016)
He, Q., et al.: Efficient keyword search for building service-based systems based on dynamic programming. In: Maximilien, M., Vallecillo, A., Wang, J., Oriol, M. (eds.) ICSOC 2017. LNCS, vol. 10601, pp. 462–470. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69035-3_33
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
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 (2014)
Liang, T., Chen, L., Wu, J., Dong, H., Bouguettaya, A.: Meta-path based service recommendation in heterogeneous information networks. In: Sheng, Q.Z., Stroulia, E., Tata, S., Bhiri, S. (eds.) ICSOC 2016. LNCS, vol. 9936, pp. 371–386. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46295-0_23
Lin, C., Kalia, A., Xiao, J., Vukovic, M., Anerousis, N.: NL2API: a framework for bootstrapping service recommendation using natural language queries. In: 2018 IEEE ICWS, pp. 235–242. IEEE (2018)
Samanta, P., Liu, X.: Recommending services for new mashups through service factors and top-k neighbors. In: 2017 IEEE ICWS, pp. 381–388. IEEE (2017)
Sun, Y., Han, J.: Mining heterogeneous information networks: a structural analysis approach. ACM SIGKDD Explor. Newsl. 14(2), 20–28 (2013)
Wei, C., Fan, Y., Zhang, J., Lin, H.: A-HSG: Neural attentive service recommendation based on high-order social graph. In: 2020 IEEE ICWS, pp. 338–346. IEEE (2020)
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 (2014)
Xiao, Y., et al.: Structure reinforcing and attribute weakening network based API recommendation approach for mashup creation. In: 2020 IEEE International Conference on Web Services (ICWS), pp. 541–548. IEEE (2020)
Xie, F., Li, S., Chen, L., Xu, Y., Zheng, Z.: Generative adversarial network based service recommendation in heterogeneous information networks. In: 2019 IEEE International Conference on Web Services (ICWS), pp. 265–272. IEEE (2019)
Yao, L., Wang, X., Sheng, Q.Z., Ruan, W., Zhang, W.: Service recommendation for mashup composition with implicit correlation regularization. In: IEEE International Conference on Web Services, pp. 217–224 (2015)
Zhang, M., Zhao, J., Dong, H., Deng, K., Liu, Y.: A knowledge graph based approach for mobile application recommendation. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds.) ICSOC 2020. LNCS, vol. 12571, pp. 355–369. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65310-1_25
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Lima, E., Liu, X. (2021). A Structure Alignment Deep Graph Model for Mashup Recommendation. In: Hacid, H., Kao, O., Mecella, M., Moha, N., Paik, Hy. (eds) Service-Oriented Computing. ICSOC 2021. Lecture Notes in Computer Science(), vol 13121. Springer, Cham. https://doi.org/10.1007/978-3-030-91431-8_44
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DOI: https://doi.org/10.1007/978-3-030-91431-8_44
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