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Volume 42 Issue 9
Sep.  2016
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Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(9): 1892-1902.
ZHANG Wancai, LIU Xudong, GUO Xiaohuiet al. Dynamic Web service recommendation based on tensor factorization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(9): 1892-1902. doi: 10.13700/j.bh.1001-5965.2015.0582(in Chinese)
Citation: ZHANG Wancai, LIU Xudong, GUO Xiaohuiet al. Dynamic Web service recommendation based on tensor factorization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(9): 1892-1902. doi: 10.13700/j.bh.1001-5965.2015.0582(in Chinese)
shu

Dynamic Web service recommendation based on tensor factorization

doi: 10.13700/j.bh.1001-5965.2015.0582

  • School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

Funds:  National Natural Science Foundation of China (61370057); National High Technology Research and Development Program of China (2012AA011203); National Key Basic Research Program of China (2014CB340304)
  • Received Date: 08 Sep 2015
  • Publish Date: 20 Sep 2016
    Abstract
  • In the area of Web service computing, in order to select a suitable service for users in a large number of Web services and API with the identical function，the issue of Web service recommendation is becoming more and more critical. At present, in the quality of service (QoS) based service recommendation systems, the hypothesis of the system model is a two-dimensional static model which is composed of dyadic relationship between users and service interaction. However, in view of the practical application, the QoS value is affected by many factors, and a tensor model is proposed to describe the factors which affect the QoS. Then, we propose a method to discover the latent factors that govern the associations among these multi-type objects of QoS. A new recommendation approach based on tensor factorization is proposed to address the issue of Web service QoS value prediction with considering Web service invocation time. The experimental results show that compared with six related algorithms, the mean absolute error (MAE) of the proposed tensor factorization algorithm is reduced by 20%-50%, and our model can be used to describe more factors and to dynamically recommend Web service.

     

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