Main Reference PaperService Rating Prediction by Exploring Social Mobile Users Geographical Locations, IEEE Transactions on Big Data, 2016 [J2EE/Hadoop].
  • To solve the problem of sparsity problem and rating prediction in social users, an algorithm is proposed named Location Based Rating Prediction (LBRP) which combining three factors: user-item geographical connection, user-user geographical connection, and interpersonal interest similarity. The recommender system is improved using geographical location of users.

+ Description
  • To solve the problem of sparsity problem and rating prediction in social users, an algorithm is proposed named Location Based Rating Prediction (LBRP) which combining three factors: user-item geographical connection, user-user geographical connection, and interpersonal interest similarity. The recommender system is improved using geographical location of users.

  • To improve the accuracy and applicability of recommender systems.

  • To achieve the scalability.

+ Aim & Objectives
  • To improve the accuracy and applicability of recommender systems.

  • To achieve the scalability.

  • To further enhance the recommendation, check-in behaviors of users will be deeply explored by considering the factor of their multi-activity centers and the attribute of POIs.

+ Contribution
  • To further enhance the recommendation, check-in behaviors of users will be deeply explored by considering the factor of their multi-activity centers and the attribute of POIs.

  • Java JDK 1.8, Hadoop 1.2.1, MySQL 5.5.40

  • Netbeans 8.0.1, J2EE, Hadoop

+ Software Tools & Technologies
  • Java JDK 1.8, Hadoop 1.2.1, MySQL 5.5.40

  • Netbeans 8.0.1, J2EE, Hadoop

  • B.E / B.Tech / M.E / M.Tech

+ Project Recommended For
  • B.E / B.Tech / M.E / M.Tech

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