Movie Recommendation System Using k-clique and Association Rule Mining

Abstract

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.