Collaborative Filtering based Recommender System using Restricted Boltzmann Machines (제한 볼츠만 기계를 이용한 협력필터링 기반 추천 시스템)

Soojung Lee (이수정)

doi:10.9708/jksci.2020.25.09.101

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.

KEYWORDS

Collaborative Filtering, Recommender System, Deep Learning, Neural Network, Restricted Boltzmann Machine

Citation status

* References for papers published after 2023 are currently being built.

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KJCKorea
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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Collaborative Filtering based Recommender System using Restricted Boltzmann Machines

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Collaborative Filtering based Recommender System using Restricted Boltzmann Machines

ABSTRACT

KEYWORDS

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REFERENCES (20) * References for papers published after 2023 are currently being built.

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* References for papers published after 2023 are currently being built.