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Collaborative Filtering based Recommender System using Restricted Boltzmann Machines

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2020, 25(9), pp.101-108
  • DOI : 10.9708/jksci.2020.25.09.101
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 4, 2020
  • Accepted : September 6, 2020
  • Published : September 29, 2020

Soojung Lee 1

1경인교육대학교

Accredited

ABSTRACT

Recommender system is a must-have feature of e-commerce, since it provides customers with convenience in selecting products. Collaborative filtering is a widely-used and representative technique, where it gives recommendation lists of products preferred by other users or preferred by the current user in the past. Recently, researches on the recommendation system using deep learning artificial intelligence technologies are actively being conducted to achieve performance improvement. This study develops a collaborative filtering based recommender system using restricted Boltzmann machines of the deep learning technology by utilizing user ratings. Moreover, a learning parameter update algorithm is proposed for learning efficiency and performance. Performance evaluation of the proposed system is made through experimental analysis and comparison with conventional collaborative filtering methods. It is found that the proposed algorithm yields superior performance than the basic restricted Boltzmann machines.

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