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Personalized Movie Recommendation System Combining Data Mining with the k-Clique Method

  • Vilakone, Phonexay (Dept. of Computer Sciences and Engineering, Soonchunhyang University) ;
  • Xinchang, Khamphaphone (Dept. of Computer Sciences and Engineering, Soonchunhyang University) ;
  • Park, Doo-Soon (Dept. of Computer Software Engineering, Soonchunhyang University)
  • Received : 2019.01.28
  • Accepted : 2019.08.25
  • Published : 2019.10.31

Abstract

Today, most approaches used in the recommendation system provide correct data prediction similar to the data that users need. The method that researchers are paying attention and apply as a model in the recommendation system is the communities' detection in the big social network. The outputted result of this approach is effective in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice and test data.

Keywords

Association Rule Mining;k-Cliques;Recommendation System

Acknowledgement

Supported by : Institute for Information & communications Technology Promotion (IITP), National Research Foundation of Korea

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