Movie Recommendation System Using k-clique and Association Rule Mining
Various recommender systems have been presented in an effort to get better preciseness. In order to further improve more accuracy, the k-clique methodology, which is used to analyze social networks was introduced in the recommendation system and the result shows the k-clique method is effective in improving accuracy. In this paper, we propose a recommendation system using k-cliques and association rule mining with the best accuracy. To estimate the performance, the maximal clique method, collaborative filtering methods are monitored using the k nearest neighbors, the k-clique method, and the k-clique and association rule mining are used to evaluate the MovieLens data. The performance outputs show that the k-cliques and association rule mining get better the preciseness of the movie recommendation system than any other methods used in this experiment.
KeywordsAssociation rule mining Collaborative filtering using a k nearest neighbor k-cliques method Maximal clique method Movie recommendation system
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2014-1-00720) supervised by the IITP (Institute for Information & communications Technology Promotion) and the National Research Foundation of Korea (No. NRF-2017R1A2B1008421).
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