TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Deep Learning-based Evolutionary Recommendation Model for Heterogeneous Big Data Integration |
| 영문제목(English Title) |
Deep Learning-based Evolutionary Recommendation Model for Heterogeneous Big Data Integration |
| 저자(Author) |
Hyun Yoo
Kyungyong Chung
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| 원문수록처(Citation) |
VOL 14 NO. 09 PP. 3730 ~ 3744 (2020. 09) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual’s importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model. |
| 키워드(Keyword) |
Data Mining
Deep Learning
Recommendation
Multimedia
Data Integration
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| 파일첨부 |
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