Web service recommendation is a challenging task when the number of services and service consumers are growing rapidly on the Internet. Previous research used information retrieve methods, such as keyword search and semantic matching, to speculate the intent of service consumers. The intent is matched with contents or topics of existing data. These methods help service consumers to select appropriate services according to their needs. However, service evolution over time and topic correlation has not been given sufficient attention. Thus we propose a service recommendation approach that is able to extract service evolution patterns from history statistic data and correlated topics from semantic service descriptions. To this end, time series prediction is used to obtain evolution patterns; Latent Dirichlet Allocation (LDA) is used to model the extracted topics. Experiments results show that our approach has higher precision than existing methods.


Service recommendation Trend Prediction Latent Dirichlet Allocation 



This work was supported by Scientific projects of higher school of Inner Mongolia [NJZY009], Open Foundation of State Key Laboratory of Networking and Switching Technology (SKLNST-2016-1-01), Programs of Higher-level talents of Inner Mongolia University [215005145143], Natural Science Foundation of Inner Mongolia Autonomous Region [2015BS0603].


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Copyright information

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

Authors and Affiliations

  1. 1.Inner Mongolia UniversityHohhotChina
  2. 2.State Key Laboratory of Networking and Switching TechnologyBUPTBeijingChina
  3. 3.University of Illinois at ChicagoChicagoUSA

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