Main Reference PaperA Content-Based Recommender System for Computer Science Publications, Journal of Knowledge-Based Systems, 2018 [Java]
  • A content-based journal recommendation system recommends a suitable journals or conferences with a priority order. It consists of two modules: feature selection module and softmax regression module. Feature vector space is generated in feature selection module and feature vectors are used to train softmax regressor. Finally, a softmax regression model uses a three class-recommendation solution to find out the candidate journal or conference.

Description
  • A content-based journal recommendation system recommends a suitable journals or conferences with a priority order. It consists of two modules: feature selection module and softmax regression module. Feature vector space is generated in feature selection module and feature vectors are used to train softmax regressor. Finally, a softmax regression model uses a three class-recommendation solution to find out the candidate journal or conference.

  • Reducing computational complexity.

  • To improve the recommendation accuracy.

Aim & Objectives
  • Reducing computational complexity.

  • To improve the recommendation accuracy.

  • Effective feature selection techniques is proposed to further improves the performance of recommendation system.

Contribution
  • Effective feature selection techniques is proposed to further improves the performance of recommendation system.

  • 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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