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Movie Recommendation System Based on Users' Personal Information and Movies Rated Using the Method of k-Clique and Normalized Discounted Cumulative Gain

Authors
Vilakone, PhonexayXinchang, KhamphaphonePark, Doo-Soon
Issue Date
Apr-2020
Publisher
한국정보처리학회
Keywords
Association Rule Mining; k-Cliques; Normalized Discounted Cumulative Gain; Recommendation System
Citation
JIPS(Journal of Information Processing Systems), v.16, no.2, pp 494 - 507
Pages
14
Journal Title
JIPS(Journal of Information Processing Systems)
Volume
16
Number
2
Start Page
494
End Page
507
URI
https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/18615
DOI
10.3745/JIPS.04.0169
ISSN
1976-913X
2092-805X
Abstract
This study proposed the movie recommendation system based on the user's personal information and movies rated using the method of k-clique and normalized discounted cumulative gain. The main idea is to solve the problem of cold-start and to increase the accuracy in the recommendation system further instead of using the basic technique that is commonly based on the behavior information of the users or based on the best-selling product. The personal information of the users and their relationship in the social network will divide into the various community with the help of the k-clique method. Later, the ranking measure method that is widely used in 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 new users. Ultimately, the result of the experiment in this paper serves as a guarantee that the proposed method offers substantial finding in raw data sets by increasing accuracy to 87.28% compared to the three most successful methods used in this experiment, and that it can solve the problem of cold-start.
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