Abstract
With the rapid development of cloud computing technique, cloud service recommendation has attracted significant research interest nowadays. The main task of cloud service recommendation is to provide a list of new cloud services for users to satisfy their requirements and the key of accurate recommendation is the complete and accurate identification of users’ preference. In this study, we propose a cloud service recommendation approach based on collaborative filtering via exploring user usage history. This approach first computes user similarity using an improved cosine similarity method, which is adjusted by cloud service popularity. Then several similar users are selected as user neighbors. At last, it predicts the possibility that active users invoke candidate services according to user neighbors. Experimental results show that the proposed approach performs better than existing approaches in terms of recommendation accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
T. Erl, R.S. Puttini, Z. Mahmood et al., Cloud computing: concepts, technology & architecture, in Cloud Computing (2013)
X.W. Zhang, H.E. Ke-Qing, J. Wang et al., A survey of personalized web service recommendation. Comput. Eng. Sci. (2013)
Z. Zheng, H. Ma, M.R. Lyu et al., QoS-aware cloud service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)
M. Chen, Y. Ma, A hybrid approach to cloud service recommendation based on QoS-aware rating and ranking. Comput. Sci. (2015)
J. Liu, M. Tang, Z. Zheng et al., Location-aware and personalized collaborative filtering for cloud service recommendation. IEEE Trans. Serv. Comput. 9(5), 1–1 (2016)
X. Wang, J. Zhu, Z. Zheng et al., A spatial-temporal QoS prediction approach for time-aware cloud service recommendation. ACM Trans. Cloud 10(1), 1–25 (2016)
Y. Xu, J. Yin, S. Deng et al., Context-aware QoS prediction for web service recommendation and selection. Expert. Syst. Appl. 53(C), 75–86 (2016)
Y.M. Afify, I.F. Moawad, N.L. Badr et al., Enhanced similarity measure for personalized cloud services recommendation. Concurr. Comput. Pract. Exp. 29 (2017)
G. Kang, J. Liu, M. Tang et al., AWSR: active cloud service recommendation based on usage history, in IEEE International Conference on Cloud Services (IEEE, 2012), pp. 186–193
X. Zhang, K. He, C. Wang et al., Interest-driven cloud service recommendation based on MFI-7, in IEEE International Conference on Services Computing (IEEE, 2013), p. 438
L. Liu, F. Lecue, N. Mehandjiev, Semantic content-based recommendation of software services using context, in ACM Transactions on the Cloud (2013)
B. Cao, J. Liu, M. Tang et al., Cloud API recommendation based on user usage history and reputation evaluation. Chin. J. Polym. Sci. 48(2), 77–98 (2015)
X. Zhang, K. He, J. Wang et al., Cloud service recommendation based on watchlist via temporal and tag preference fusion, in IEEE International Conference on Cloud Services (2014), pp. 281–288
G. Kang, M. Tang, J. Liu et al., Diversifying cloud service recommendation results via exploring service usage history. IEEE Trans. Serv. Comput., 1–1 (2015)
L.Y. Hao, J. Wang, Collaborative filtering TopN-recommendation algorithm based on item popularity. Comput. Eng. Des. 34(10), 3497–3501 (2013)
W. Xia, L. He, L. Ren et al., A new collaborative filtering approach utilizing item’s popularity, in IEEE International Conference on Industrial Engineering and Engineering Management (IEEE, 2008), pp. 1480–1484
Acknowledgements
The work was supported by the Key Program of National Natural Science Foundation of China (No. 71631003) and the General Program of National Natural Science Foundation of China (No. 71771169).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Ff., Chen, Fz., Li, Mq. (2019). A Collaborative Filtering Method for Cloud Service Recommendation via Exploring Usage History. In: Huang, G., Chien, CF., Dou, R. (eds) Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018. Springer, Singapore. https://doi.org/10.1007/978-981-13-3402-3_75
Download citation
DOI: https://doi.org/10.1007/978-981-13-3402-3_75
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3401-6
Online ISBN: 978-981-13-3402-3
eBook Packages: Business and ManagementBusiness and Management (R0)