Main Reference PaperSocial-aware Sequential Modeling of User Interests: A Deep Learning Approach, IEEE Transactions on knowledge and data engineering, 2018 [Java/Python/R].
  • The Social-Aware Long Short-Term Memory (SA-LSTM) is proposed to predict the user interest on items. It exploits the stacked LSTM on sequence learning and Stacked Denoising AutoEncoders (SDAEs) on social modeling.

+ Description
  • The Social-Aware Long Short-Term Memory (SA-LSTM) is proposed to predict the user interest on items. It exploits the stacked LSTM on sequence learning and Stacked Denoising AutoEncoders (SDAEs) on social modeling.

  • To forecast the user interest on the types of items

  • To enhance the accuracy of prediction

+ Aim & Objectives
  • To forecast the user interest on the types of items

  • To enhance the accuracy of prediction

  • The proposed system improved by using the technique to avert the overfitting issue

+ Contribution
  • The proposed system improved by using the technique to avert the overfitting issue

  • Operating system: Ubuntu / Windows

  • Java/Python/R

+ Software Tools & Technologies
  • Operating system: Ubuntu / Windows

  • Java/Python/R

  • 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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