Main Reference PaperA Multi-objective Optimization Approach for Question Routing in Community Question Answering Sevices, IEEE Transactions on Knowledge and Data Engineering, April 2017 [Java].
  • In order to optimize the answering possibility and answer quality of routed users, a multi-objective learning-to-rank approach for question routing (MLQR) is proposed. A tagword topic model (TTM) is proposed to derive topical representations of questions and to develop features for each question-user pair.

+ Description
  • In order to optimize the answering possibility and answer quality of routed users, a multi-objective learning-to-rank approach for question routing (MLQR) is proposed. A tagword topic model (TTM) is proposed to derive topical representations of questions and to develop features for each question-user pair.

  • To route new questions to suitable answerers, who have both high possibility and high ability to answer the questions.

  • To optimize the answering possibility and answer quality of routed users

+ Aim & Objectives
  • To route new questions to suitable answerers, who have both high possibility and high ability to answer the questions.

  • To optimize the answering possibility and answer quality of routed users

  • Effective extraction technique is proposed to extract platform level features and thread level features.

+ Contribution
  • Effective extraction technique is proposed to extract platform level features and thread level features.

  • Java JDK 1.8, MySQL 5.5.40.

  • Netbeans 8.0.1, & J2SE.

+ Software Tools & Technologies
  • Java JDK 1.8, MySQL 5.5.40.

  • Netbeans 8.0.1, & J2SE.

  • 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-Depending on the complexity of the project and requirements.

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

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