Skip to main content
Log in

Deep knowledge-aware framework for web service recommendation

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Web services are products in the era of service-oriented computing and cloud computing. Considering the information overload problem arising from the task of selecting web services, a recommendation system is by far the most effective solution for performing such selections. However, users calling a limited number of services will cause severe data sparseness and a weak correlation with services. In addition, fully mining the semantic features and knowledge features of the text description is also a major problem that needs to be solved urgently. This paper proposes a deep knowledge-aware approach which introduces knowledge graph and knowledge representation into web service recommendation for the first time. We solve the data sparse problem and optimize the user’s feature representation. In this approach, an attention module is introduced to model the impact of tags for the candidate services on different words of user queries, and a deep neural network is used to model the high-level features of user-service invocation behaviors. The results of experiments demonstrate that the proposed approach can achieve better recommendation performance than other state-of-the-art methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. http://www.programmableweb.com/.

  2. http://test.wikidata.org.

References

  1. (2017) Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems“,”pubMedId“:”27411231

  2. Ayyadevara VK (2018) Word2vec. In: Pro Machine Learning Algorithms, Apress, Berkeley, CA, pp. 167–178

  3. Aznoli F, Navimipour NJ (2017) Cloud services recommendation: reviewing the recent advances and suggesting the future research directions. J Netw Comput Appl 77:73–86

    Article  Google Scholar 

  4. Bai B, Fan Y, Huang K, Tan W, Xia B, Chen S (2015) Service recommendation for mashup creation based on time-aware collaborative domain regression. In: 2015 IEEE International Conference on Web Services, IEEE, pp 209–216

  5. Bai B, Fan Y, Tan W, Zhang J (2020) Dltsr: a deep learning framework for recommendations of long-tail web services. IEEE Comput Architect Lett 13(01):73–85

    Google Scholar 

  6. Barod P, Bhamare M, Patankar R (2016) A novel approach for web service recommendation. In: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies, pp 1–4

  7. Botangen KA, Yu J, Sheng QZ, Han Y, Yongchareon S (2020) Geographic-aware collaborative filtering for web service recommendation. Expert Syst Appl 151:113347

    Article  Google Scholar 

  8. Brank J, Leban G, Grobelnik M (2017) Annotating documents with relevant wikipedia concepts. In: Proceedings of the Slovenian Conference on data mining and data warehouses

  9. Cao B, Liu J, Tang M, Zheng Z, Wang G (2013) Mashup service recommendation based on user interest and social network. In: 2013 IEEE 20th International Conference on web services, IEEE, pp 99–106

  10. Cao B, Liu XF, Rahman MM, Li B, Liu J, Tang M (2017) Integrated content and network-based service clustering and web apis recommendation for mashup development. IEEE Trans Serv Comput 13(1):99–113

    Article  Google Scholar 

  11. Cao B, Liu J, Wen Y, Li H, Xiao Q, Chen J (2019) Qos-aware service recommendation based on relational topic model and factorization machines for iot mashup applications. J Parallel Distributed Comput 132:177–189

    Article  Google Scholar 

  12. Chen X, Zheng Z, Yu Q, Lyu MR (2014) Web service recommendation via exploiting location and qos information. IEEE Trans Parallel Distrib Syst 25(7):1913–1924

    Article  Google Scholar 

  13. Church KWARD (2017) Word2vec. Natural Lang Eng 23(01):155–162

    Article  Google Scholar 

  14. 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

  15. Emami H (2019) A graph-based approach to person name disambiguation in web. ACM Trans Manag Inf Syst (TMIS) 10(2):1–25

    Article  Google Scholar 

  16. Fellbaum C (2010) Wordnet In: Theory and applications of ontology computer applications, Springer, NewYork pp 231–243

  17. Guermah H, Fissaa T, Hafiddi H, Nassar M (2018) Exploiting semantic web services in the development of context-aware systems. Procedia Comput Sci 127:398–407

    Article  Google Scholar 

  18. Hao Y, Fan Y, Tan W, Zhang J (2017) Service recommendation based on targeted reconstruction of service descriptions. In: 2017 IEEE International Conference on web services (ICWS), IEEE, pp 285–292

  19. Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:151106939

  20. Kalaï A, Zayani CA, Amous I, Sèdes F (2016) Expertise and trust–aware social web service recommendation. In: International Conference on service-oriented computing, Springer, pp 517–533

  21. Kalaï A, Zayani CA, Amous I, Abdelghani W, Sèdes F (2018) Social collaborative service recommendation approach based on user’s trust and domain-specific expertise. Fut Gen Comput Syst 80:355–367

    Article  Google Scholar 

  22. Kang G, Tang M, Liu J, Liu XF, Cao B (2016) Diversifying web service recommendation results via exploring service usage history. IEEE Comput Architect Lett 9(04):566–579

    Google Scholar 

  23. Ketkar N (2017) Convolutional neural networks. Springer, Newyork

    Book  Google Scholar 

  24. Li C, Zhang R, Huai J, Guo X, Sun H (2013) A probabilistic approach for web service discovery. In: 2013 IEEE international Conference on services computing, IEEE, pp 49–56

  25. Liang X, Qin A, Tang K, Tan KC (2019) Qos-aware web service selection with internal complementarity. IEEE Trans Serv Comput 12(2):276–289

    Article  Google Scholar 

  26. Liu X, Fulia I (2015) Incorporating user, topic, and service related latent factors into web service recommendation. In: 2015 IEEE international Conference on web services, IEEE, pp 185–192

  27. Ma Y, Geng X, Wang J (2020) A deep neural network with multiplex interactions for cold-start service recommendation. IEEE Trans Eng Manag 68(1):105–119

    Article  Google Scholar 

  28. Moro A, Raganato A, Navigli R (2014) Entity linking meets word sense disambiguation: a unified approach. Trans Assoc Comput Linguist 2:231–244

    Article  Google Scholar 

  29. Paliwal AV (2012) Semantics-based automated service discovery. IEEE Trans Serv Comput 5(2):260–275

    Article  Google Scholar 

  30. Platzer C, Dustdar S (2005) A vector space search engine for web services. In: Third European Conference on web services (ECOWS’05), IEEE, p 9

  31. Pradeep D, Sundar C (2020) QAOC: Novel query analysis and ontology-based clustering for data management in Hadoop. Future Gener Comput Syst 108:849–860

  32. Rodriguez-Mier P, Pedrinaci C, Lama M, Mucientes M (2016) An integrated semantic web service discovery and composition framework. IEEE Comput Archit Lett 9(04):537–550

    Google Scholar 

  33. Roman D, Kopecky J, Vitvar T, Domingue J, Fensel D (2015) Wsmo-lite and hrests: lightweight semantic annotations for web services and restful apis. Web Sem Sci Serv Agents World Wide Web 31:39–58

    Article  Google Scholar 

  34. Shi M, Liu J, Zhou D, Tang M, Xie F, Zhang T (2016) A probabilistic topic model for mashup tag recommendation. In: 2016 IEEE international Conference on web services (ICWS), IEEE, pp 444–451

  35. Shi M, Liu J, Zhou D, Tang M, Cao B (2017) We-lda: a word embeddings augmented lda model for web services clustering. In: 2017 IEEE international Conference on web services (icws), IEEE, pp 9–16

  36. Shi M, Liu J et al (2018) Functional and contextual attention-based lstm for service recommendation in mashup creation. IEEE Trans Parallel Dist Syst 30(5):1077–1090

    Article  Google Scholar 

  37. Shi M, Liu J (2018) Functional and contextual attention-based LSTM for service recommendation in Mashup creation. IEEE Transactions on Parallel and Distributed Systems 30(5):1077-1090

  38. Tajbakhsh MS, Bagherzadeh J (2016) Microblogging hash tag recommendation system based on semantic tf-idf: Twitter use case. In: 2016 IEEE 4th international Conference on future internet of things and cloud workshops (FiCloudW), IEEE, pp 252–257

  39. Tsalgatidou A, Pilioura T (2002) An overview of standards and related technology in web services. Distrib Parallel Database 12(2–3):135–162

    Article  Google Scholar 

  40. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA, Bottou L (2010) Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(12)

  41. Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on artificial intelligence, 28

  42. 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 

  43. Wulam A, Wang Y, Zhang D, Sang J, Yang A (2019) A recommendation system based on fusing boosting model and dnn model. CMC-Comput Mater Continua 60(3):1003–1013

    Article  Google Scholar 

  44. Xia B, Fan Y, Tan W, Huang K, Zhang J, Wu C (2015) Category-aware api clustering and distributed recommendation for automatic mashup creation. IEEE Tran Serv Comput 8(5):674–687

    Article  Google Scholar 

  45. 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 

  46. 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 

  47. 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

  48. 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 

  49. Zhong Y, Fan Y, Huang K, Tan W, Zhang J (2015) Time-aware service recommendation for mashup creation. IEEE Trans Serv Comput 3(8):356–368

    Article  Google Scholar 

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

    Article  Google Scholar 

  51. Zhou Y, Ushiama T (2019) Lstm-based recommendation approach for interaction records. In: International Conference on ubiquitous information management and communication, Springer, pp 950–962

Download references

Acknowledgements

This research is supported by 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; and the Program for New Century Excellent Talents in the University of Ministry of Education of China under Grant No. NCET-10-0239.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rongen Yan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dang, D., Chen, C., Li, H. et al. Deep knowledge-aware framework for web service recommendation. J Supercomput 77, 14280–14304 (2021). https://doi.org/10.1007/s11227-021-03832-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03832-2

Keywords