Skip to main content
Log in

Qos-based web service selection using time-aware collaborative filtering: a literature review

  • Regular Paper
  • Published:
Computing Aims and scope Submit manuscript

Abstract

The proliferation of available Web services presents a big challenge in selecting suitable services. Various methods have been devised to predict Quality of Service (QoS) values, aiming to address the service selection problem. However, these methods encounter numerous limitations that hinder their prediction accuracy. A key issue stems from the dynamic nature of the service environment, leading to fluctuations in QoS values due to factors like network load and hardware issues. To mitigate these challenges, QoS selection methods have leveraged contextual information from the surrounding environments, such as service invocation time, user, and service locations. Among these methods, Collaborative Filtering (CF) has gained notable importance. In recent years, several CF methods have incorporated service invocation time into their prediction processes, giving rise to what is commonly known as time-aware CF methods. Despite the increasing adoption of time-aware CF methods, there remains a notable absence of a dedicated and comprehensive literature review on this topic. Addressing this gap, this paper conducts an analysis of the literature, reviewing the forty (40) most prominent studies in this domain. It offers a thematic categorization of these studies along with an insightful analysis outlining their objectives, advantages, and limitations. The review also identifies key research gaps and proposes potential directions for future investigations. Overall, this literature review serves as an up-to-date resource for researchers engaged in service-oriented computing research.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

No datasets were generated or analysed during the current study.

References

  1. Zhang Y, Zheng Z, Lyu MR (2011) Wspred: a time-aware personalized qos prediction framework for web services. In: 2011 IEEE 22nd International Symposium on Software Reliability Engineering, pp 210–219 . IEEE

  2. Tong E, Niu W, Liu J (2021) A missing qos prediction approach via time-aware collaborative filtering. IEEE Trans Serv Comput 15(6):3115–3128

    Article  Google Scholar 

  3. Zhu J, He P, Xie Q, Zheng Z, Lyu MR (2017) Carp: context-aware reliability prediction of black-box web services. In: 2017 IEEE International Conference on Web Services (ICWS), pp 17–24. IEEE

  4. Xiong R, Wang J, Li Z, Li B, Hung PC (2018) Personalized lstm based matrix factorization for online qos prediction. In: 2018 IEEE International Conference on Web Services (ICWS), pp 34–41. IEEE

  5. Zhu J, He P, Zheng Z, Lyu MR (2017) Online qos prediction for runtime service adaptation via adaptive matrix factorization. IEEE Trans Parallel Distrib Syst 28(10):2911–2924

    Article  Google Scholar 

  6. Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70

    Article  Google Scholar 

  7. Shao L, Zhang J, Wei Y, Zhao J, Xie B, Mei H (2007) Personalized qos prediction forweb services via collaborative filtering. In: Ieee International Conference on Web Services (icws 2007), pp 439–446. IEEE

  8. Zheng Z, Xiaoli L, Tang M, Xie F, Lyu MR (2020) Web service qos prediction via collaborative filtering: A survey. IEEE Trans Serv Comput 15(4):2455–2472

    Article  Google Scholar 

  9. Ghafouri SH, Hashemi SM, Hung PC (2020) A survey on web service qos prediction methods. IEEE Trans Serv Comput 15(4):2439–2454

    Article  Google Scholar 

  10. Mezni H, Fayala M (2018) Time-aware service recommendation: taxonomy, review, and challenges. Softw: Pract Exp 48(11):2080–2108

    Google Scholar 

  11. Puri AS, Bhonsle M (2015) A survey of web service recommendation techniques based on qos values. Int J Adv Res Comput Commun Eng 4(12)

  12. Syu Y, Wang C-M (2021) Qos time series modeling and forecasting for web services: a comprehensive survey. IEEE Trans Netw Serv Manage 18(1):926–944

    Article  Google Scholar 

  13. Vinagre J, Jorge AM, Gama J (2015) An overview on the exploitation of time in collaborative filtering. Wiley Interdis Rev: Data Min Knowl Disc 5(5):195–215

    Google Scholar 

  14. Campos PG, Díez F, Cantador I (2014) Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model User-Adap Inter 24:67–119

    Article  Google Scholar 

  15. Wu L, He X, Wang X, Zhang K, Wang M (2022) A survey on accuracy-oriented neural recommendation: from collaborative filtering to information-rich recommendation. IEEE Trans Knowl Data Eng 35(5):4425–4445

    Google Scholar 

  16. Hu Y, Peng Q, Hu X, Yang R (2014) Time aware and data sparsity tolerant web service recommendation based on improved collaborative filtering. IEEE Trans Serv Comput 8(5):782–794

    Article  Google Scholar 

  17. Yin G, Cui X, Dong H, Dong Y (2013) Web service evaluation method based on time-aware collaborative filtering. In: International Conference on Intelligent Data Engineering and Automated Learning, pp 76–84. Springer

  18. Yu C, Huang L (2014) Time-aware collaborative filtering for qos-based service recommendation. In: 2014 IEEE International Conference on Web Services, pp 265–272. IEEE

  19. Yu C, Huang L (2016) A web service qos prediction approach based on time-and location-aware collaborative filtering. SOCA 10(2):135–149

    Article  MathSciNet  Google Scholar 

  20. Li J, Wang J, Sun Q, Zhou A (2017) Temporal influences-aware collaborative filtering for qos-based service recommendation. In: 2017 IEEE International Conference on Services Computing (SCC), pp 471–474. IEEE

  21. Yu C, Huang L (2017) Clucf: a clustering cf algorithm to address data sparsity problem. SOCA 11(1):33–45

    Article  Google Scholar 

  22. Meng S, Li Q, Chen S, Yu S, Qi L, Lin W, Xu X, Dou W (2018) Temporal-sparsity aware service recommendation method via hybrid collaborative filtering techniques. In: International Conference on Service-oriented Computing, pp 421–429. Springer

  23. Fan X, Hu Y, Zheng Z, Wang Y, Brézillon P, Chen W (2017) Casr-tse: context-aware web services recommendation for modeling weighted temporal-spatial effectiveness. IEEE Trans Serv Comput 14(1):58–70

    Google Scholar 

  24. Zheng Z, Zhang Y, Lyu MR (2012) Investigating qos of real-world web services. IEEE Trans Serv Comput 7(1):32–39

    Article  Google Scholar 

  25. Zhang W, Sun H, Liu X, Guo X (2014) Temporal qos-aware web service recommendation via non-negative tensor factorization. In: Proceedings of the 23rd International Conference on World Wide Web, pp 585–596

  26. Zhang W, Sun H, Liu X, Guo, X (2014) Incorporating invocation time in predicting web service qos via triadic factorization. In: 2014 IEEE International Conference on Web Services, pp 145–152. IEEE

  27. Zhang W, Sun H, Liu X (2014) An incremental tensor factorization approach for web service recommendation. In: 2014 IEEE International Conference on Data Mining Workshop, pp 346–351. IEEE

  28. Meng S, Zhou Z, Huang T, Li D, Wang S, Fei F, Wang W, Dou W (2016) A temporal-aware hybrid collaborative recommendation method for cloud service. In: 2016 IEEE International Conference on Web Services (ICWS), pp 252–259. IEEE

  29. Luo X, Wu H, Yuan H, Zhou M (2019) Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Trans Cybern 50(5):1798–1809

    Article  Google Scholar 

  30. Ye F, Lin Z, Chen C, Zheng Z, Huang H (2021) Outlier-resilient web service qos prediction. In: Proceedings of the Web Conference 2021, pp 3099–3110

  31. Tian G, Wang J, He K, Hung PC, Sun C (2014) Time-aware web service recommendations using implicit feedback. In: 2014 IEEE International Conference on Web Services, pp 273–280. IEEE

  32. Li S, Wen J, Luo F, Ranzi G (2018) Time-aware qos prediction for cloud service recommendation based on matrix factorization. IEEE Access 6:77716–77724

    Article  Google Scholar 

  33. You M, Xin X, Shangguang W, Jinglin L, Qibo S, Fangchun Y (2015) Qos evaluation for web service recommendation. China Commun 12(4):151–160

    Article  Google Scholar 

  34. Cheng T, Wen J, Xiong Q, Zeng J, Zhou W, Cai X (2019) Personalized web service recommendation based on qos prediction and hierarchical tensor decomposition. IEEE Access 7:62221–62230

    Article  Google Scholar 

  35. Ma Y, Wang S, Yang F, Chang RN (2015) Predicting qos values via multi-dimensional qos data for web service recommendations. In: 2015 IEEE International Conference on Web Services, pp 249–256. IEEE

  36. Silic M, Delac G, Srbljic S (2014) Prediction of atomic web services reliability for qos-aware recommendation. IEEE Trans Serv Comput 8(3):425–438

    Article  Google Scholar 

  37. Wu C, Qiu W, Wang X, Zheng Z, Yang X (2016) Time-aware and sparsity-tolerant qos prediction based on collaborative filtering. In: 2016 IEEE International Conference on Web Services (ICWS), pp 637–640. IEEE

  38. Jin Y, Guo W, Zhang Y (2019) A time-aware dynamic service quality prediction approach for services. Tsinghua Sci Technol 25(2):227–238

    Article  Google Scholar 

  39. Chen L, Ying H, Qiu Q, Wu J, Dong H, Bouguettaya A (2016) Temporal pattern based qos prediction. In: International Conference on Web Information Systems Engineering, pp 223–237. Springer

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

    Article  Google Scholar 

  41. Kai D, Bin G, Kuang L (2016) A time-aware weighted-svm model for web service qos prediction. In: International Conference on Collaborative Computing: Networking, Applications and Worksharing, pp 302–311. Springer

  42. Wu X, Fan Y, Zhang J, Lin H, Zhang J (2019) Qf-rnn: Qi-matrix factorization based rnn for time-aware service recommendation. In: 2019 IEEE International Conference on Services Computing (SCC), pp 202–209. IEEE

  43. Zhou J, Guo X, Yin C (2020) Recurrent factorization machine with self-attention for time-aware service recommendation. In: 2020 6th International Conference on Big Data Computing and Communications (BIGCOM), pp 189–197. IEEE

  44. Zhang Y, Yin C, Lu Z, Yan D, Qiu M, Tang Q (2019) Recurrent tensor factorization for time-aware service recommendation. Appl Soft Comput 85:105762

    Article  Google Scholar 

  45. Zhou Q, Wu H, Yue K, Hsu C-H (2019) Spatio-temporal context-aware collaborative qos prediction. Future Gener Comput Syst 100:46–57

    Article  Google Scholar 

  46. Li B, Ye C, Yu X, Zhou H, Huang C (2021) Qos prediction based on temporal information and request context. SOCA 15(3):231–244

    Article  Google Scholar 

  47. Li M, Lu Q, Zhang M, Liang X (2019) A multi-task service recommendation model considering dynamic and static qos. In: 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), pp 760–767. IEEE

  48. Zou G, Li T, Jiang M, Hu S, Cao C, Zhang B, Gan Y, Chen Y (2022) Deeptsqp: temporal-aware service qos prediction via deep neural network and feature integration. Knowl-Based Syst 241:108062

    Article  Google Scholar 

  49. Hu Y, Peng Q, Hu X, Yang R (2015) Web service recommendation based on time series forecasting and collaborative filtering. In: 2015 Ieee International Conference on Web Services, pp 233–240. IEEE

  50. Ding S, Li Y, Wu D, Zhang Y, Yang S (2018) Time-aware cloud service recommendation using similarity-enhanced collaborative filtering and arima model. Decis Support Syst 107:103–115

    Article  Google Scholar 

  51. Ma H, Zhu H, Hu Z, Tang W, Dong P (2017) Multi-valued collaborative qos prediction for cloud service via time series analysis. Future Gener Comput Syst 68:275–288

    Article  Google Scholar 

  52. Syu Y, Wang CM (2019) An empirical investigation of real-world qos of web services. In: International Conference on Services Computing, pp 48–65. Springer

  53. Chen Z, Sun Y, You D, Li F, Shen L (2020) An accurate and efficient web service qos prediction model with wide-range awareness. Future Gener Comput Syst 109:275–292

    Article  Google Scholar 

  54. Shen L, Pan M, Liu L, You D, Li F, Chen Z (2020) Contexts enhance accuracy: on modeling context aware deep factorization machine for web api qos prediction. IEEE Access 8:165551–165569

    Article  Google Scholar 

  55. Syu Y, Kuo J-Y, Fanjiang Y-Y (2017) Time series forecasting for dynamic quality of web services: an empirical study. J Syst Softw 134:279–303

    Article  Google Scholar 

  56. Hussain W, Hussain FK, Saberi M, Hussain OK, Chang E (2018) Comparing time series with machine learning-based prediction approaches for violation management in cloud slas. Future Gener Comput Syst 89:464–477

    Article  Google Scholar 

  57. Cavallo B, Di Penta M, Canfora G (2010) An empirical comparison of methods to support qos-aware service selection. In: Proceedings of the 2nd International Workshop on Principles of Engineering Service-Oriented Systems, pp 64–70

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Contributions

Authors’ contribution will be provide later or added to the paper

Corresponding author

Correspondence to Ezdehar Jawabreh.

Ethics declarations

Conflict of interest

The authors declare no Conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jawabreh, E., Taweel, A. Qos-based web service selection using time-aware collaborative filtering: a literature review. Computing (2024). https://doi.org/10.1007/s00607-024-01283-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00607-024-01283-0

Keywords

Mathematics Subject Classification

Navigation