Main Reference PaperClubCF: A Clustering-based Collaborative Filtering Approach for Big Data Application, IEEE Transactions on Emerging Topics in Computing, 2014 .
  • This work presents a ClubCF approach for big data applications relevant to service recommendation. Before applying CF technique, services are merged into some clusters via an AHC algorithm. Then the rating similarities between services within the same cluster are computed. As the number of services in a cluster is much less than that of in the whole system, ClubCF costs less online computation time. Moreover, as the ratings of services in the same cluster are more relevant with each other than with the ones in other clusters, prediction based on the ratings of the services in the same cluster will be more accurate than based on the ratings of all similar or dissimilar services in all clusters.

+ Description
  • This work presents a ClubCF approach for big data applications relevant to service recommendation. Before applying CF technique, services are merged into some clusters via an AHC algorithm. Then the rating similarities between services within the same cluster are computed. As the number of services in a cluster is much less than that of in the whole system, ClubCF costs less online computation time. Moreover, as the ratings of services in the same cluster are more relevant with each other than with the ones in other clusters, prediction based on the ratings of the services in the same cluster will be more accurate than based on the ratings of all similar or dissimilar services in all clusters.

  • To recommend services to requested user.

  • To handle large amount of services in effective manner.

  • To decrease the number of services that need to be processed .

+ Aim & Objectives
  • To recommend services to requested user.

  • To handle large amount of services in effective manner.

  • To decrease the number of services that need to be processed .

  • This proposed work may enhance first with the respect of service similarity, semantic analysis may be performed on the description text of service. Second, with respect to users, mining their implicit interests from usage records or reviews may be a complement to the explicit interests

+ Contribution
  • This proposed work may enhance first with the respect of service similarity, semantic analysis may be performed on the description text of service. Second, with respect to users, mining their implicit interests from usage records or reviews may be a complement to the explicit interests

  • Java JDK 1.8, HBase 0.94.16, Hadoop 1.2.1.

  • Netbeans 8.0.1, J2EE, Hadoop, HBase, Pentaho.

+ Software Tools & Technologies
  • Java JDK 1.8, HBase 0.94.16, Hadoop 1.2.1.

  • Netbeans 8.0.1, J2EE, Hadoop, HBase, Pentaho.

  • 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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