Movie Recommendation System Based on Users’ Personal Information and Movies Rated Using the Method of k-Clique and Normalized Discounted Cumulative Gain
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초록
This study proposed the movie recommendation system based on the user’s personal information and moviesrated using the method of kclique and normalized discounted cumulative gain. The main idea is to solve theproblem of coldstart and to increase the accuracy in the recommendation system further instead of using thebasic technique that is commonly based on the behavior information of the users or based on the bestsellingproduct. The personal information of the users and their relationship in the social network will divide into thevarious community with the help of the kclique method. Later, the ranking measure method that is widely usedin the searching engine will be used to check the top ranking movie and then recommend it to the new users. We strongly believe that this idea will prove to be significant and meaningful in predicting demand for newusers. Ultimately, the result of the experiment in this paper serves as a guarantee that the proposed methodoffers substantial finding in raw data sets by increasing accuracy to 87.28% compared to the three mostsuccessful methods used in this experiment, and that it can solve the problem of coldstart.
Abstract
This study proposed the movie recommendation system based on the user’s personal information and moviesrated using the method of kclique and normalized discounted cumulative gain. The main idea is to solve theproblem of coldstart and to increase the accuracy in the recommendation system further instead of using thebasic technique that is commonly based on the behavior information of the users or based on the bestsellingproduct. The personal information of the users and their relationship in the social network will divide into thevarious community with the help of the kclique method. Later, the ranking measure method that is widely usedin the searching engine will be used to check the top ranking movie and then recommend it to the new users. We strongly believe that this idea will prove to be significant and meaningful in predicting demand for newusers. Ultimately, the result of the experiment in this paper serves as a guarantee that the proposed methodoffers substantial finding in raw data sets by increasing accuracy to 87.28% compared to the three mostsuccessful methods used in this experiment, and that it can solve the problem of coldstart.
참고문헌 (25)
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