Main Reference PaperService Rating Prediction by Exploring Social Mobile Users Geographical Locations, IEEE Transactions on Big Data, 2016 [Python/Hadoop].
  • To solve sparsity problem and for rating prediction in social users, an algorithm is proposed named as Location Based Rating Prediction (LBRP) which combines three factors such as 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 sparsity problem and for rating prediction in social users, an algorithm is proposed named as Location Based Rating Prediction (LBRP) which combines three factors such as 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.

  • 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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