Main Reference PaperUser-Service Rating Prediction by Exploring Social Users’ Rating Behaviors, IEEE Transactions On Multimedia, March 2016 [Java].
  • The user-service rating prediction is combined by some social network factors: such as: 1) personal interest: It denotes user’s individuality of rating items, especially for the experienced users. 2) Interpersonal interest similarity: It make connections between user and his/her friend’s latent feature vectors. 3) Interpersonal Rating Behavior Similarity: It denotes the user’s rating behavior habits and his/her rating standards.

+ Description
  • The user-service rating prediction is combined by some social network factors: such as: 1) personal interest: It denotes user’s individuality of rating items, especially for the experienced users. 2) Interpersonal interest similarity: It make connections between user and his/her friend’s latent feature vectors. 3) Interpersonal Rating Behavior Similarity: It denotes the user’s rating behavior habits and his/her rating standards.

  • To solve the data sparsity and cold start problem.

  • To investigate the relations between personality characteristics and user rating behavior.

  • The goal of matrix factorization is to learn these latent features and exploit them to predict user-service ratings.

+ Aim & Objectives
  • To solve the data sparsity and cold start problem.

  • To investigate the relations between personality characteristics and user rating behavior.

  • The goal of matrix factorization is to learn these latent features and exploit them to predict user-service ratings.

  • To improve the rating prediction further, the Context Based Social Users Rating Behaviors Analysis is contributed.

+ Contribution
  • To improve the rating prediction further, the Context Based Social Users Rating Behaviors Analysis is contributed.

  • 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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