Main Reference PaperSimilarity-maintaining Privacy Preservation and Location-aware Low-rank Matrix Factorization for QoS Prediction based Web Service Recommendation, IEEE Transactions on Service Computing, 2018 [Java/J2EE]
  • A proposed QoS-based Web service recommendation considers namely user privacy preservation and missing value prediction, and provide a complete privacy-preserving the QoS prediction framework. A similarity-maintaining privacy preservation scheme is combined with the location-aware low-rank matrix factorization schemes to predict the missing QoS values.

Description
  • A proposed QoS-based Web service recommendation considers namely user privacy preservation and missing value prediction, and provide a complete privacy-preserving the QoS prediction framework. A similarity-maintaining privacy preservation scheme is combined with the location-aware low-rank matrix factorization schemes to predict the missing QoS values.

  • To recommend the high-quality services to the users.

  • To achieve the user’s privacy.

Aim & Objectives
  • To recommend the high-quality services to the users.

  • To achieve the user’s privacy.

  • A user’s location information is protected.

Contribution
  • A user’s location information is protected.

  • Operating system: Ubuntu / Windows

  • Language: Java/J2EE

Software Tools & Technologies
  • Operating system: Ubuntu / Windows

  • Language: Java/J2EE

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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