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Multi-representation web service recommendation system based on attention mechanism

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Abstract

Recently, mashup developers seek to integrate multiple services with complementary functionalities from a large amount of web services. With so many available web services, it is difficult for developers to choose the right one to develop new mashups. Therefore, it is critical to create and recommend appropriate web services for mashup developers based on their development needs. In the past, various deep models have been proposed to facilitate web service recommendation based on semantic matching of textual descriptions. However, existing deep approaches mainly match global semantic representations while ignoring descriptive structure and tag information. In this paper, we propose a multi-representation web service recommendation model, which simultaneously extracts global, local and tag representations of the description and tag information, respectively. Moreover, we propose a tag-driven attention mechanism to guide the process of information extraction. Experiments over a real-world dataset demonstrate that our proposed service recommendation algorithm can achieve remarkable performance.

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References

  1. Agarwal N, Sikka G, Awasthi LK (2022) A systematic literature review on web service clustering approaches to enhance service discovery, selection and recommendation. Comput Sci Rev 45:100498

    Article  Google Scholar 

  2. Wu H, Duan Y, Yue K, Zhang L (2021) Mashup-oriented web API recommendation via multi-model fusion and multi-task learning. IEEE Trans Serv Comput 15(6):3330–3343

    Article  Google Scholar 

  3. Almarimi N, Ouni A, Bouktif S, Mkaouer MW, Kula RG, Saied MA (2019) Web service API recommendation for automated mashup creation using multi-objective evolutionary search. Appl Soft Comput 85:105830

    Article  Google Scholar 

  4. Shi M, Tang Y, Liu J (2019) Ta-blstm: tag attention-based bidirectional long short-term memory for service recommendation in mashup creation. In: 2019 international joint conference on neural networks (IJCNN), IEEE, pp 1–8

  5. Qi L, Song H, Zhang X, Srivastava G, Xu X, Yu S (2021) Compatibility-aware web API recommendation for mashup creation via textual description mining. ACM Trans Multimidia Comput Commun Appl 17(1s):1–19

    Article  Google Scholar 

  6. Liang W, Xie S, Cai J, Xu J, Hu Y, Xu Y, Qiu M (2021) Deep neural network security collaborative filtering scheme for service recommendation in intelligent cyber-physical systems. IEEE Internet Things J 9(22):22123–22132

    Article  Google Scholar 

  7. Xiong R, Wang J, Zhang N, Ma Y (2018) Deep hybrid collaborative filtering for web service recommendation. Expert Syst Appl 110:191–205

    Article  Google Scholar 

  8. Geetha G, Safa M, Fancy C, Saranya D (2018) A hybrid approach using collaborative filtering and content based filtering for recommender system. In: Journal of physics: conference series, vol. 1000, IOP Publishing, p 012101

  9. Paliwal AV, Shafiq B, Vaidya J, Xiong H, Adam N (2011) Semantics-based automated service discovery. IEEE Trans Serv Comput 5(2):260–275

    Article  Google Scholar 

  10. Rodriguez-Mier P, Pedrinaci C, Lama M, Mucientes M (2015) An integrated semantic web service discovery and composition framework. IEEE Trans Serv Comput 9(4):537–550

    Article  Google Scholar 

  11. Roman D, Kopeckỳ J, Vitvar T, Domingue J, Fensel D (2015) Wsmo-lite and hrests: lightweight semantic annotations for web services and restful APIs. J Web Semant 31:39–58

    Article  Google Scholar 

  12. Zhong Y, Fan Y, Tan W, Zhang J (2016) Web service recommendation with reconstructed profile from mashup descriptions. IEEE Trans Autom Sci Eng 15(2):468–478

    Article  Google Scholar 

  13. Zhang N, Wang J, Ma Y (2017) Mining domain knowledge on service goals from textual service descriptions. IEEE Trans Serv Comput 13(3):488–502

    Article  Google Scholar 

  14. Yao L, Wang X, Sheng QZ, Ruan W, Zhang W (2015) Service recommendation for mashup composition with implicit correlation regularization. In: 2015 IEEE international conference on web services, IEEE, pp 217–224

  15. Cao J, Lu Y, Zhu N (2016) Service package recommendation for mashup development based on a multi-level relational network. In: Service-oriented computing: 14th international conference, ICSOC 2016, Banff, AB, Canada, October 10-13, 2016, Proceedings 14, Springer, pp 666–674

  16. Dong X, Yu L, Wu Z, Sun Y, Yuan L, Zhang F (2017) A hybrid collaborative filtering model with deep structure for recommender systems. In: Proceedings of the AAAI conference on artificial intelligence, vol. 31

  17. Wu X, Cheng B, Chen J (2015) Collaborative filtering service recommendation based on a novel similarity computation method. IEEE Trans Serv Comput 10(3):352–365

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Kang G, Tang M, Liu J, Liu X, Cao B (2015) Diversifying web service recommendation results via exploring service usage history. IEEE Trans Serv Comput 9(4):566–579

    Article  Google Scholar 

  20. Jia Y (2019) Attention mechanism in machine translation. In: Journal of physics: conference series, IOP Publishing, vol. 1314, p 012186

  21. Dai B, Li J, Xu R (2020) Multiple positional self-attention network for text classification. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34, pp 7610–7617

  22. Basiri ME, Nemati S, Abdar M, Cambria E, Acharya UR (2021) Abcdm: an attention-based bidirectional CNN-RNN deep model for sentiment analysis. Futur Gener Comput Syst 115:279–294

    Article  Google Scholar 

  23. Huang Y, Chen J, Zheng S, Xue Y, Hu X (2021) Hierarchical multi-attention networks for document classification. Int J Mach Learn Cybern 12:1639–1647

    Article  Google Scholar 

  24. Zhou X, Wan X, Xiao J (2016) Attention-based lstm network for cross-lingual sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 247–256

  25. Ding J, Li Y, Ni H, Yang Z (2020) Generative text summary based on enhanced semantic attention and gain-benefit gate. IEEE Access 8:92659–92668

    Google Scholar 

  26. Ying H, Zhuang F, Zhang F, Liu Y, Xu G, Xie X, Xiong H, Wu J (2018) Sequential recommender system based on hierarchical attention network. In: IJCAI international joint conference on artificial intelligence

  27. Cen Y, Zhang J, Zou X, Zhou C, Yang H, Tang J (2020) Controllable multi-interest framework for recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2942–2951

  28. Ma J, Zhao Z, Yi X, Chen J, Hong L, Chi EH (2018) Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1930–1939

  29. Tang J, Belletti F, Jain S, Chen M, Beutel A, Xu C, H Ch E (2019) Towards neural mixture recommender for long range dependent user sequences. In: The World Wide Web conference, pp 1782–1793

  30. Kim Y (2014) Convolutional neural networks for sentence classification. Eprint Arxiv

  31. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781

  32. Bai B, Fan Y, Tan W, Zhang J (2017) Dltsr: a deep learning framework for recommendations of long-tail web services. IEEE Trans Serv Comput 13(1):73–85

    Article  Google Scholar 

  33. Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5–6):602–610

    Article  PubMed  Google Scholar 

  34. Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 1480–1489

  35. Xi D, Zhuang F, Song B, Zhu Y, Chen S, Hong D, Chen T, Gu X, He Q (2020) Neural hierarchical factorization machines for user’s event sequence analysis. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 1893–1896

  36. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

  37. Zhu Y, Liu Y, Xie R, Zhuang F, Hao X, Ge K, Zhang X, Lin L, Cao J (2021) Learning to expand audience via meta hybrid experts and critics for recommendation and advertising. arXiv preprint arXiv:2105.14688

  38. Liu Z, Niu X-F, Zhuang C, Tan Y, Mu Y, Gu J, Zhang G (2020) Two-stage audience expansion for financial targeting in marketing. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 2629–2636

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Funding

This work was supported by The National Key Research and Development Program of China under Grant No.2020YFC1523303; the Key Research and Development Program of Qinghai Province under Grant No.2020-SF-140; the National Natural Science Foundation of China under Grant No. 61672102, No. 61073034, No. 61370064 and No. 60940032; the National Social Science Foundation of China under Grant No.BCA150050; the Program for New Century Excellent Talents in the University of Ministry of Education of China under Grant No. NCET-10-0239; the Open Project Sponsor of Beijing Key Laboratory of Intelligent Communication Software and Multimedia under Grant No.ITSM201493; and the Science Foundation of Ministry of Education of China and China Mobile Communications Corporation under Grant No. MCM20130371.

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing Normal University, No.19 Xinjiekouwai St., Beijing, 100875, China

    Depeng Dang, Bilin Guo, Tingting Fang & Ying Zhang

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  1. Depeng Dang
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  2. Bilin Guo
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  3. Tingting Fang
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  4. Ying Zhang
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Contributions

DD is the corresponding author of this paper. BG contributed to methodology, writing—review and editing. TF contributed to writing—review and editing, and revision. YZ contributed to revision.

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Correspondence to Depeng Dang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Cite this article

Dang, D., Guo, B., Fang, T. et al. Multi-representation web service recommendation system based on attention mechanism. Knowl Inf Syst (2024). https://doi.org/10.1007/s10115-024-02061-2

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