Personalized Movie Recommendation System Combining Data Mining with the k-Clique Method
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초록
Today, most approaches used in the recommendation system provide correct data prediction similar to the datathat users need. The method that researchers are paying attention and apply as a model in the recommendationsystem is the communities’ detection in the big social network. The outputted result of this approach is effectivein improving the exactness. Therefore, in this paper, the personalized movie recommendation system thatcombines data mining for the k-clique method is proposed as the best exactness data to the users. The proposedapproach was compared with the existing approaches like k-clique, collaborative filtering, and collaborativefiltering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significantexactness data compared to the existing approach. In the experiment, the MovieLens data were used as practiceand test data.
Abstract
Today, most approaches used in the recommendation system provide correct data prediction similar to the datathat users need. The method that researchers are paying attention and apply as a model in the recommendationsystem is the communities’ detection in the big social network. The outputted result of this approach is effectivein improving the exactness. Therefore, in this paper, the personalized movie recommendation system thatcombines data mining for the k-clique method is proposed as the best exactness data to the users. The proposedapproach was compared with the existing approaches like k-clique, collaborative filtering, and collaborativefiltering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significantexactness data compared to the existing approach. In the experiment, the MovieLens data were used as practiceand test data.
참고문헌 (26)
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