A Novel Service Recommendation Approach Considering the User’s Trust Network

  • Guoqiang Li
  • Zibin Zheng
  • Haifeng WangEmail author
  • Zifen Yang
  • Zuoping Xu
  • Li Liu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)


Web services are ever increasingly published on the network as core components of Service-oriented architecture (SOA). An attendant problem is how to help users select their satisfied services that meet their functional and non-functional requirements from the mass services. Service recommendation technology is adopted and studied as an effective approach currently. This paper focuses on the user’s trust network, where the users share their experience and rating for the invoked services. To attack the data sparsity and cold-start problems in the user-service rating matrix, an improved random walk algorithm is proposed. Firstly, we employ the non-negative matrix factorization method to compute the similarities between users and services separately. Then our method introduces the trust relationship in iterations of the random walk to select the trust users accurately. At last, the real dataset is used to validate our approach. Experimental results show the effectiveness of our approach compared with the state-of-art algorithms.


Web service Service recommendation Social network Random walk 



This work was funded by the Natural Science Foundation of Shandong Province (NSFS Grant No. ZR2014FL013) and the Independent Innovation and Achievements Transformation Special Project of Shandong Province (No. 2014ZZCX02702). The authors acknowledge the support of the Opening Fund of Shandong Provincial Key Laboratory for Network Based Intelligent Computing.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Guoqiang Li
    • 1
  • Zibin Zheng
    • 2
  • Haifeng Wang
    • 1
    Email author
  • Zifen Yang
    • 1
  • Zuoping Xu
    • 1
  • Li Liu
    • 1
  1. 1.School of InformaticsLinyi UniversityLinyiChina
  2. 2.Mobile Internet and Financial Big Data LabSun Yat-Sen UniversityGuangzhouChina

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