Design and Implementation of a User-based Collaborative Filtering Application using Apache Mahout - based on MongoDB -

Jun-Ho Lee (이준호); Joo Kyung-Soo (주경수)

doi:10.9708/jksci.2018.23.04.089

Design and Implementation of a User-based Collaborative Filtering Application using Apache Mahout - based on MongoDB -

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2018, 23(4), pp.89-95
DOI : 10.9708/jksci.2018.23.04.089
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : February 19, 2018
Accepted : April 3, 2018
Published : April 30, 2018

Jun-Ho Lee ¹, Joo Kyung-Soo ¹

¹순천향대학교

Accredited

ABSTRACT

It is not easy for the user to find the information that is appropriate for the user among the suddenly increasing information in recent years. One of the ways to help individuals make decisions in such a lot of information is the recommendation system. Although there are many recommendation methods for such recommendation systems, a representative method is collaborative filtering. In this paper, we design and implement the movie recommendation system on user-based collaborative filtering of apache mahout based on mongoDB. In addition, Pearson correlation coefficient is used as a method of measuring the similarity between users. We evaluate Precision and Recall using the MovieLens 100k dataset for performance evaluation.

KEYWORDS

Mahout, User-based Collaborative Filtering, Movie Recommender System, MongoDB

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

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

Design and Implementation of a User-based Collaborative Filtering Application using Apache Mahout - based on MongoDB -

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* 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

Design and Implementation of a User-based Collaborative Filtering Application using Apache Mahout - based on MongoDB -

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KEYWORDS

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