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ARIR: an intent recognition-based approach for API recommendation

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Abstract

With the prevalence of service composition, how to recommend API services that meet the Mashup requirements for developers has become a challenging in the field of service computing. Existing works on Web service recommendation typically conduct service recommendation based on the semantic requirements and interaction relationships of Web services, while ignoring the construction intent of Mashup creators. Moreover, they often only focus on the impact of single intent on recommendation, neglecting the diverse requirements of Mashup creators at different intent-levels. As a result, the recommendation models fail to comprehensively understand the construction intent of Mashup creators, which affects the quality and effectiveness of Web service recommendation. To address this problem, this paper proposes an intent recognition-based API recommendation method, denoted as ARIR. This method utilizes the annotation information of Mashup and API to analyse the creation intent of Mashup and the functional intent of API, thereby providing more accurate API with high-quality to Mashup creators. Firstly, it decouples the representations of Mashup and API at different intent-levels and independently initializes the node representations at each intent-level. Secondly, it trains the vector representations at each intent-level using a decoupled graph convolutional neural network module and obtains the representation vectors of Mashup/API at different intent-levels with attention weights. Then, it aggregates the intents of Mashup and API using the Mashup-API interaction relationships, resulting in the final node representations of Mashup/API. Furthermore, it constructs a similarity heterogeneous network by calculating the Mashup-to-Mashup similarity and API-to-API similarity, updating the node representations by using the Mashup and API feature matrices and adjacency matrices, and obtaining the final recommendation prediction results by using a fully connected layer. Finally, the experimental results conducted on real-world Web service datasets demonstrate that the ARIR outperforms the best-performing baseline method with Recall@20 of 1.1% and NDCG@20 of 3.3%.

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No datasets were generated or analysed during the current study.

References

  1. Hasan, M.H., Jaafar, J., Hassan, M.F.: Fuzzy-based clustering of web services’ quality of service: a review. J. Commun. 9(1), 81–90 (2014)

    Article  Google Scholar 

  2. Cao, B., Liu, X.F., 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. 13(1), 99–113 (2020)

    Article  Google Scholar 

  3. Elgazzar, K., Hassan, A.E., Martin, P.: Clustering wsdl documents to bootstrap the discovery of web services. In: 2010 IEEE International Conference on Web Services, pp. 147–154 (2010). https://doi.org/10.1109/ICWS.2010.31

  4. Chen, L., Wang, Y., Yu, Q., Zheng, Z., Wu, J.: Wt-lda: user tagging augmented lda for web service clustering. In: Proceedings 11 Service-Oriented Computing: 11th International Conference, ICSOC 2013, Berlin, December 2–5, 2013, pp. 162–176 (2013)

  5. 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). https://doi.org/10.1109/ICWS.2014.50

  6. 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: 2017 IEEE International Conference on Services Computing (SCC), pp. 225–232 (2017). https://doi.org/10.1109/SCC.2017.36

  7. Chen, T., Liu, J., Cao, B., Peng, Z., Wen, Y., Li, R.: Web service recommendation based on word embedding and topic model. In: 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom), pp. 903–910 (2018). https://doi.org/10.1109/BDCloud.2018.00133

  8. Cao, B., Liu, J., Tang, M., Zheng, Z., Wang, G.: Mashup service recommendation based on usage history and service network. Int. J. Web Serv. Res. (IJWSR) 10(4), 82–101 (2013)

    Article  Google Scholar 

  9. Kang, G., Liu, J., Xiao, Y., Cao, B., Xu, Y., Cao, M.: Neural and attentional factorization machine-based web api recommendation for mashup development. IEEE Trans. Netw. Serv. Manag. 18(4), 4183–4196 (2021)

    Article  Google Scholar 

  10. Cao, B., Peng, M., Zhang, L., Qing, Y., Tang, B., Kang, G., Liu, J.: Web service recommendation via integrating heterogeneous graph attention network representation and fibinet score prediction. IEEE Trans. Serv. Comput. (2023)

  11. Cao, B., Zhang, L., Peng, M., Qing, Y., Kang, G., Liu, J.: Web service recommendation via combining bilinear graph representation and xdeepfm quality prediction. IEEE Trans. Netw. Serv. Manag. (2023)

  12. Ma, J., Cui, P., Kuang, K., Wang, X., Zhu, W.: Disentangled graph convolutional networks. In: International Conference on Machine Learning, pp. 4212–4221 (2019)

  13. Wang, X., Jin, H., Zhang, A., He, X., Xu, T., Chua, T.-S.: Disentangled graph collaborative filtering. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1001–1010 (2020). https://doi.org/10.1145/3397271.3401137

  14. Wu, J., Shi, W., Cao, X., Chen, J., Lei, W., Zhang, F., Wu, W., He, X.: Disenkgat: knowledge graph embedding with disentangled graph attention network. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 2140–2149 (2021). https://doi.org/10.1145/3459637.3482424

  15. Klusch, M., Fries, B., Sycara, K.: Owls-mx: a hybrid semantic web service matchmaker for owl-s services. J. Web Semant. 7(2), 121–133 (2009)

    Article  Google Scholar 

  16. Xu, S., Raahemi, B.: A semantic-based service discovery framework for collaborative environments. Int. J. Simul. Model. 15(1), 83–96 (2016)

    Article  Google Scholar 

  17. Yao, L., Sheng, Q.Z., Ngu, A.H., Yu, J., Segev, A.: Unified collaborative and content-based web service recommendation. IEEE Trans. Serv. Comput. 8(3), 453–466 (2014)

    Article  Google Scholar 

  18. 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 (2012)

    Article  Google Scholar 

  19. Chen, X., Zheng, Z., Yu, Q., Lyu, M.R.: Web service recommendation via exploiting location and qos information. IEEE Trans. Parallel Distrib. Syst. 25(7), 1913–1924 (2013)

    Article  Google Scholar 

  20. Wang, X., Zhu, J., Zheng, Z., Song, W., Shen, Y., Lyu, M.R.: A spatial-temporal qos prediction approach for time-aware web service recommendation. ACM Trans. Web (TWEB) 10(1), 1–25 (2016)

    Article  Google Scholar 

  21. Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining, pp. 995–1000 (2010). https://doi.org/10.1109/ICDM.2010.127

  22. Rendle, S.: Factorization machines with libfm. ACM Trans. Intell. Syst. Technol. (TIST) 3(3), 1–22 (2012)

    Article  Google Scholar 

  23. LV, F.: Web services reputation evaluation model based on qos and user recommendation. Yanshan University, pp. 18–26 (2010)

  24. Gao, W., Chen, L., Wu, J., Gao, H.: Manifold-learning based api recommendation for mashup creation. In: 2015 IEEE International Conference on Web Services, pp. 432–439 (2015). https://doi.org/10.1109/ICWS.2015.64

  25. Gao, W., Chen, L., Wu, J., Bouguettaya, A.: Joint modeling users, services, mashups, and topics for service recommendation. In: 2016 IEEE International Conference on Web Services (icws), pp. 260–267 (2016). https://doi.org/10.1109/ICWS.2016.41

  26. 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)

    Article  Google Scholar 

  27. Liu, X., Fulia, I.: Incorporating user, topic, and service related latent factors into web service recommendation. In: 2015 IEEE International Conference on Web Services, pp. 185–192 (2015). https://doi.org/10.1109/ICWS.2015.34

  28. Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 425–434 (2017). https://doi.org/10.1145/3018661.3018665

  29. Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016). https://doi.org/10.1609/aaai.v30i1.9971

  30. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks (2016). arXiv:1609.02907

  31. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks (2017). arXiv:1710.10903

  32. Kang, G., Liu, J., Cao, B., Cao, M.: Nafm: neural and attentional factorization machine for web api recommendation. In: 2020 IEEE International Conference on Web Services (ICWS), pp. 330–337 (2020). https://doi.org/10.1109/ICWS49710.2020.00050

  33. Guo, J., Huang, K., Yi, X., Zhang, R.: Lgd-gcn: Local and global disentangled graph convolutional networks (2021). arXiv:2104.11893

  34. Wang, Y., Tang, S., Lei, Y., Song, W., Wang, S., Zhang, M.: Disenhan: disentangled heterogeneous graph attention network for recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1605–1614 (2020). https://doi.org/10.1145/3340531.3411996

  35. Lv, C., Jiang, W., Hu, S.: A novel graph model-based api recommendation system. J. Comput. Sci. Technol. 38(11), 2172–2187 (2015)

    Google Scholar 

  36. He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: Lightgcn: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 639–648 (2020). https://doi.org/10.1145/3397271.3401063

  37. Wang, S., Cao, B., Xie, X., Zhang, L., Kang, G., Liu, J.: An api recommendation method based on beneficial interaction. In: International Conference on Collaborative Computing: Networking, Applications and Worksharing, pp. 58–72 (2022)

  38. Tishby, N., Pereira, F.C., Bialek, W.: The information bottleneck method (2000). arXiv:physics/0004057

  39. Louizos, C., Welling, M., Kingma, D.P.: Learning sparse neural networks through \( l_0 \) regularization (2017). arXiv:1712.01312

  40. Wang, X., He, X., Wang, M., Feng, F., Chua, T.-S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 165–174 (2019). https://doi.org/10.1145/3331184.3331267

  41. Chang, J., Gao, C., He, X., Jin, D., Li, Y.: Bundle recommendation with graph convolutional networks. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1673–1676 (2020). https://doi.org/10.1145/3397271.3401198

  42. Schlichtkrull, M., Kipf, T.N., Bloem, P., Van Den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Proceedings 15 the Semantic Web: 15th International Conference, ESWC 2018, Heraklion, June 3–7, 2018, pp. 593–607 (2018)

  43. Hu, Z., Dong, Y., Wang, K., Sun, Y.: Heterogeneous graph transformer. In: Proceedings of the Web Conference 2020, pp. 2704–2710 (2020)

  44. Chen, M., Huang, C., Xia, L., Wei, W., Xu, Y., Luo, R.: Heterogeneous graph contrastive learning for recommendation. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pp. 544–552 (2023)

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Acknowledgements

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. 62376062), the National Key R &D Program of China (2018YFB1402800), Hunan Provincial Natural Science Foundation of China (No. 2022JJ30020), the Science and Technology Innovation Program of Hunan Province (No. 2023sk2081). Buqing Cao and Xiang Xie are the corresponding author of this paper.

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  1. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China

    Buqing Cao, Siyuan Wang, Xiang Xie, Qian Peng, Yating Yi & Zhenlian Peng

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  1. Buqing Cao
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  2. Siyuan Wang
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  4. Qian Peng
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Buqing Cao designs the idea of this paper, Siyuan Wang performs the experiment, Xiang xie and Qian Peng write the main manuscript text, Yating Yi and Zhenlian Peng revise the main manuscript text.

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Correspondence to Buqing Cao or Xiang Xie.

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Cao, B., Wang, S., Xie, X. et al. ARIR: an intent recognition-based approach for API recommendation. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04520-5

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