Abstract
With the rapid development of service computing technology, more and more companies and organizations are encapsulating and publishing their operational data or resources to the Internet in the form of Web services, resulting in an exponential increase in the number of Web services. To automatically generate or recommend a group of Web services according to the user's natural language requirements description, to build a Mashup application to meet the user's requirement is a hot topic in service computing. Some researchers enhance the quality of Web service recommendation by using auxiliary information into the recommendation system. However, they mainly focus on adding external information (e.g., pre-training of external corpora) to enhance semantic features, while some internal statistical features of the corpus such as word distribution on labels and frequency are not fully exploited. Compared to other exterior knowledge, statistical features are naturally compatible with Web service recommendation tasks. To fully exploit the statistical features, this paper proposes a Web service recommendation method based on adaptive gate network and xDeepFM model. In this method, firstly, the description document of Web services is taken as the basic corpus, the semantic and statistical information in the corpus are mined by utilizing the adaptive gate network, and the statistical features are encoded by a variational encoder. Secondly, the similarity between Web services is derived from the semantic features, at the same time the popularity and co-occurrence of Web services are calculated. Thirdly, the xDeepFM model is used to mine the explicit and implicit higher-order interactions in the sparse matrix which consists of the above information to recommend Web services for Mashup application. Finally, a multiple sets of experiments based on the dataset crawled from the ProgrammableWeb have been conducted to evaluate the proposed method and the experimental result shows that the proposed method has better performance in the \(AUC\) and \(Logloss\) compared with the state-of-art baseline methods.
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References
Katal, A., Wazid, M., Goudar, R.H.: Big data: Issues, challenges, tools and good practices, ICCC2013 (2013)
Weerawarana, S., Curbera, F., et al.: Web Services Platform Architecture: SOAP, WSDL, WS-Policy, WS-Addressing, WS-BPEL, WS-Reliable Messaging and More. Prentice Hall PTR (2005)
Deng, S., Wu, H., Hu, D., Leon Zhao, J.: Service selection for composition with QoS correlations. IEEE Trans. Serv. Comput. 9(2), 291–303 (2014)
Kim, G., Trimi, S., Chung, J.: Big-data applications in the government sector. Commun. ACM 57(3), 78–85 (2014)
Milanovic, N., Malek, M.: Current solutions for web service composition. IEEE Internet Comput. 8(6), 51–59 (2004)
Huang, L., Jiang, B., Lv, S., Liu, Y., Li, D.: Survey on deep learning-based recommender systems. Chin. J. Comput. 41(7), 1619–1647 (2018)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 29(12), 426–434 (2009)
X. Wang, and Y. Wang, Improving content-based and hybrid music recommendation using deep learning, ACM Multim. 627–636 (2014)
Zeng, Z., et al.: Knowledge transfer via pre-training for recommendation: a review and prospect. Front. Big Data. 4, 602071 (2021)
Rendle, S.: Factorization Machines. In: ICDM2010 (2010)
Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: A factorization-machine based neural network for CTR prediction. In: IJCAI, pp. 1725–1731 (2017)
Li, X., Li, Z., Xie, H., Li, Q.: Merging statistical feature via adaptive gate for improved text classification. In: AAAI2021 (2021)
Lian, J., Zhou, X. et al.: xDeepFM: Combining explicit and implicit feature interactions for recommender systems. In: ACM SIGKDD2018 (2018)
Xia, B., Fan, Y., et al.: Category-aware API clustering and distributed recommendation for automatic mashup creation. IEEE Trans. Serv. Comput. 8(5), 674–687 (2015)
Bobadilla, J., Ortega, F., et al.: Recommender systems survey. Know. Based Syst. 46, 109–132 (2013)
Conneau, A., Schwenk, H., et al.: Very deep convolutional networks for text classification. In: ACL2017, pp. 1107–1116 (2017)
Shi, M., Tang, Y., Liu, J.: Functional and contextual attention-based LSTM for service recommendation in mashup creation. IEEE Trans. Parallel Distrib. Syst. 30(5), 1077–1090 (2019)
Wang, H., Yang, Z., Yu, Q.: Online reliability prediction via long short-term memory for service-oriented systems. In: ICWS2017 (2017)
Ye, H., Cao, B., et al.: Web services classification based on wide & Bi-LSTM model. IEEE Access 7, 43697–43706 (2019)
Kang, G., Liu, J., et al.: NAFM: Neural and attentional factorization machine for web API recommendation. In: ICWS2020 (2020)
Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: Estimating the click-through rate for new ads. In: WWW2007 (2007)
Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: AAAI2019 (2019)
Ye, H., Cao, B. et al.: A web services classification method based on GCN. In: ISPA2019 (2019)
Aizawa, A.: An information-theoretic perspective of TF-IDF measures. Inf. Process. Manag. 39(1), 45–65 (2003)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: ICML1997, pp.412–420 (1997)
Ramos, J.: Using TF-IDF to determine word relevance in document queries. In: ICML2013 (2013)
Acknowledgments
The authors thank the anonymous reviewers for their valuable feedback and comments. The work is supported by the National Natural Science Foundation of China (No. 61873316, 61872139, 61832014, and 61702181), Hunan Provincial Natural Science Foundation of China under grant No. 2021JJ30274, the Educational Commission of Hunan Province of China (No.20B244), and the Scientific Research Project of Huaihua University (No. HHUY2020-18). Buqing Cao and Hongfan Ye are the corresponding author of this paper.
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Tao, Z., Cao, B., Ye, H., Kang, G., Peng, Z., Wen, Y. (2023). A Web Service Recommendation Method Based on Adaptive Gate Network and xDeepFM. In: Meng, W., Lu, R., Min, G., Vaidya, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2022. Lecture Notes in Computer Science, vol 13777. Springer, Cham. https://doi.org/10.1007/978-3-031-22677-9_9
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