Group Similarity Based Hybrid Web Service Recommendation Algorithm
Xie Qi1,2(),Cui Mengtian1
1School of Computer Science and Technology, Southwest University for Nationalities, Chengdu 610225, China 2Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China
[Objective] This paper tries to solve the issues of lacking similar services or users in Web service computing due to the data sparsity of Quality of Service (QoS) recommendation. [Methods] First, we created personalized similar user and service groups according to similarity distance of the target users and services. Second, we used the group center similarities of the user and service groups to design a new hybrid recommendation algorithm(GHQR), which was tested with real-world data of 1.97 million QoS records. [Results] Compared with two traditional recommendation algorithms, the GHQR reduced the Normalized Mean Absolute Error (NMAE) by 31% and 69%. It also increased the Coverage by 105% and 163%, respectively. [Limitations] Our study only examined the response time of QoS, and more research was needed to investigate other QoS properties. [Conclusions] Comprared with WSRec and CFBUGI, the GHQR can reduce the NMAE by 26% and 7.7%. It also increased the Coverage by 188% and 4%, respectively. GHQR not only enhances the prediction accuracy but also increases the coverage significantly.
谢琪,崔梦天. 基于相似性群体的混合型Web服务推荐*[J]. 现代图书情报技术, 2016, 32(6): 80-87.
Xie Qi,Cui Mengtian. Group Similarity Based Hybrid Web Service Recommendation Algorithm. New Technology of Library and Information Service, 2016, 32(6): 80-87.
(Shao Lingshuang, Zhou Li, Zhao Junfeng, et al.Web Service QoS Prediction Approach[J]. Journal of Software, 2009, 20(8): 2062-2073.)
[2]
Zheng Z, Ma H, Lyu M R, et al.QoS-aware Web Service Recommendation by Collaborative Filtering[J]. IEEE Transactions on Service Computing, 2011, 4(2): 140-152.
[3]
Chen X, Zheng Z, Liu X, et al.Personalized QoS-aware Web Service Recommendation and Visualization[J]. IEEE Transactions on Services Computing, 2013, 6(1): 35-47.
[4]
He P, Zhu J, Zheng Z, et al.Location-based Hierarchical Matrix Factorization for Web Service Recommendation [C]. In: Proceedings of 2014 IEEE International Conference on Web Services (ICWS2014). Washington, DC: IEEE Computer Society, 2014: 297-304.
(Zhang Xuan, Liu Cong, Wang Lixia, et al.Trustworthy Web Service Recommendation Based on Collaborative Filtering[J]. Journal of Computer Applications, 2014, 34(1): 213-217.)
(Wang Haiyan, Yang Wenbin, Wang Suichang, et al.A Service Recommendation Method Based on Trustworthy Community[J]. Chinese Journal of Computers, 2014, 37(2): 301-311.)
(Jiang Bo, Zhang Xiaoxiao, Pan Weifeng.Bipartite Graph-based Service Recommendation Method Study[J]. Journal of Huazhong University of Science and Technology: Natural Science Edition, 2013, 41(S2): 93-99.)
(Lin Xiangyun, Liu Xiaoqing, Tang Mingdong, et al.An Empirical Study of Correlation Between Web Service QoS and User Location[J]. Computer Engineering and Science, 2013, 35(9): 83-88.)
[9]
Xie Q, Zheng Z, Liu L, et al.Correlation-based Top-k Recommendation for Web Services [C]. In: Proceedings of the 13th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC-2015), Liverpool, UK. 2015: 1903-1909.
(Gao Huming, Zhao Fengyue.A Hybrid Recommendation Method Combining Collaborative Filtering and Content Filtering[J]. New Technology of Library and Information Service, 2015(6): 20-26.)
(Zhu Ting, Qin Chunxiu, Li Zuhai.Research on Collaborative Filtering Personalized Recommendation Method Based on User Classification[J]. New Technology of Library and Information Service, 2015(6): 13-19.)
(Lin Yaojin, Hu Xuegang, Li Huizong.Collaborative Filtering Recommendation Algorithm Based on User Group Influence[J]. Journal of the China Society for Scientific and Technical Information, 2013, 32(3): 299-305.)
(Ying Yan, Cao Yan, Mu Xiangwei.A Hybrid Collaborative Filtering Recommender Based on Item Rating Prediction[J]. New Technology of Library and Information Service, 2015(6): 27-32.)