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, DOI：10.11925/infotech.1003-3513.2016.06.10.
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