Mining Product Relationships for Recommendation Based on Cloud Service Data

  • Yuanchun JiangEmail author
  • Cuicui Ji
  • Yang Qian
  • Yezheng Liu
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 342)


With the rapid growth of cloud services, it is more and more difficult for users to select appropriate service. Hence, an effective service recommendation method is need to offer suggestions and selections. In this paper, we propose a two- phase approach to discover related cloud services for recommendation by jointly leveraging services’ descriptive texts and their associated tags. In Phase 1, we use a non-parametric Bayesian method, DPMM to classify a large number of cloud services into an optimal number of clusters. In Phase 2, we recommend a personalized PageRank algorithm to obtain more related services for recommendation among the massive cloud service products in the same cluster. Empirical experiments on a real data set show that the proposed two-phase approach is more successful than other candidate methods for service clustering and recommendation.


Cloud service Cluster DPMM Personalized PageRank 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yuanchun Jiang
    • 1
    Email author
  • Cuicui Ji
    • 1
  • Yang Qian
    • 1
  • Yezheng Liu
    • 1
  1. 1.School of ManagementHefei University of TechnologyHefeiPeople’s Republic of China

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