Collaborative Filtering Using Restricted Boltzmann Machine and Fuzzy C-means

  • Dayal Kumar Behera
  • Madhabananda Das
  • Subhra Swetanisha
  • Bighnaraj Naik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)


Recommender system is valuable to find items as per users’ taste from a large volume of items. Various popular techniques to perform personalized recommendations are content based, collaborative, and hybrid recommender. Collaborative filtering is widely used in this domain which can be of memory based or model based. The datasets used in recommender systems are very often sparse. Hence, accurate prediction can be made by grouping users/items into cluster. In this paper, an attempt is made to cluster the users using FCM clustering algorithm, and then, RBM is used to predict the user’s preferences. Experiment is carried out on MovieLens benchmark dataset. The results depict the performance of using both FCM and RBM to build the model for recommendation.


Collaborative filtering Restricted Boltzmann Machine User-based filtering Movie recommendation 



The authors would like to express thanks to all the reviewers for valuable comments and suggestions.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Dayal Kumar Behera
    • 1
    • 2
  • Madhabananda Das
    • 1
  • Subhra Swetanisha
    • 2
  • Bighnaraj Naik
    • 3
  1. 1.Department of CSEKIIT UniversityBhubaneswarIndia
  2. 2.Department of CSETrident Academy of TechnologyBhubaneswarIndia
  3. 3.Department of Computer ApplicationVSSUTBurlaIndia

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