[논문]Deep Learning-based Evolutionary Recommendation Model for Heterogeneous Big Data Integration

Yoo, Hyun; Chung, Kyungyong

doi:10.3837/tiis.2020.09.009

Deep Learning-based Evolutionary Recommendation Model for Heterogeneous Big Data Integration 원문보기

KSII Transactions on internet and information systems : TIIS, v.14 no.9, 2020년, pp.3730 - 3744

Yoo, Hyun (Contents Convergence Software Research Center, Kyonggi University) , Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (7)

그림 Fig. 1. Heterogeneous Big Data Integration Configuration
그림 Fig. 2. Data Preprocessing and Selection Process
표 Table 1. Selected Attribute Information
그림 Fig. 3. Process of Health Status Decision using Collaborative Filtering
그림 Fig. 4. Internal Structure of an Evolutionary Recommendation Model
그림 Fig. 5. Evolutionary Recommendation System for a Heterogeneous Big-data Integration
표 Table 2. Final Performance Evaluation Results

AI 본문요약
AI-Helper

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제안 방법

In order to evaluate the performance of the deep learning-based evolutionary recommendation model, a server–client model for health platforms was designed.
The RMSE, which represents the difference between an actual value (observed value) and an estimated value, is generally used to find the difference between an estimated value or a model’s predicted value and an actual value; its definition is similar to that of the standard deviation [31]. In this study, the RMSE is specifically defined as the difference between the predicted value of the collaborative filtering model and the predicted value of the deep learning-based evolutionary recommendation model. The RMSE can be used to evaluate the accuracy of the proposed model, and it is given by Equation (3) as follows:
The raw material of the National Health and Nutrition Examination Survey includes an examination survey, a health-questionnaire survey, and a nutrition survey. The examination survey consists of a basic survey table, family history, thyroid-disease examination, lung-function examination, tuberculosis (TB) examination (chest X-ray), oral examination, eye examination, chromoscopy, otolaryngologic examination, bone-density examination, osteoarthritic examination, and muscular examination. Fig.
The selected data were applied to the clustering operation for various chronic diseases with the use of collaborative filtering. The collaborative filtering was performed on the results that were drawn from the users’ different environmental information.
The algorithmic convergence can result in higher accuracy and enhanced results, thereby making a complementary design possible. This study proposes an evolutionary recommendation model using a deep learning-based DNN algorithm, while a gradient descent-based backpropagation algorithm was applied to create an inter-node model. In order to experimentally measure the accuracy, recommendation information was extracted from the preprocessed data of the Korea National Health and Nutrition Examination Survey (KNHANES).

대상 데이터

For the client device, an LG G3 LTE-A Cat. 6 Android device (LG Electronics, ROK) was used. The H/W of the client device included the Snapdragon 805 mobile processor with 2.
The present study uses the seventh statistical data of 2018 among the datasets from 2016 to 2018 [21]. This statistical material consists of 726 items and 8,024 data points. In order to determine the significance of the healthcare data, 534 subitems that have been deemed to bear no relation to the health pattern and the dietary lives have been excluded [7,22].
Ultimately, 69 items and 4,659 records were selected. Of the 69 items, one is the current diabetes disease type, which is used as a fixed factor when the significance is determined.

이론/모형

In order to experimentally measure the accuracy, recommendation information was extracted from the preprocessed data of the Korea National Health and Nutrition Examination Survey (KNHANES). For the health platform, this study developed an algorithm using a combination of the collaborative filtering-based recommendation and the deep learning-based evolutionary recommendation model. In order to evaluate the performance, the RMSE was applied to compare the resulting values of the collaborative filtering method and the modified value obtained using the deep learning-based evolutionary recommendation model.
For the health platform, this study developed an algorithm using a combination of the collaborative filtering-based recommendation and the deep learning-based evolutionary recommendation model. In order to evaluate the performance, the RMSE was applied to compare the resulting values of the collaborative filtering method and the modified value obtained using the deep learning-based evolutionary recommendation model. According to the evaluation, the prediction method using the deep learning-based evolutionary recommendation improved the accuracy.
This study proposes an evolutionary recommendation model using a deep learning-based DNN algorithm, while a gradient descent-based backpropagation algorithm was applied to create an inter-node model. In order to experimentally measure the accuracy, recommendation information was extracted from the preprocessed data of the Korea National Health and Nutrition Examination Survey (KNHANES). For the health platform, this study developed an algorithm using a combination of the collaborative filtering-based recommendation and the deep learning-based evolutionary recommendation model.
The root mean square error (RMSE) was used to evaluate the proposed model [12]. The RMSE, which represents the difference between an actual value (observed value) and an estimated value, is generally used to find the difference between an estimated value or a model’s predicted value and an actual value; its definition is similar to that of the standard deviation [31].

성능/효과

According to the results of this experiment, the accuracy of the deep learning-based evolutionary recommendation model is greater than that of the collaborative filtering-based recommendation model, and this is attributed to the fact that the DNN structure was applied to the user’s evaluation value.
Given the results, it became evident that the algorithm combined with the DNN and collaborative filtering showed better performance than the conventional collaborative filtering which only used data analysis.
Based on large learning data, a DNN learns from items that have been properly observed, and can thereby create a proper approximation function. The technology is applied to solve problems in various areas, including image processing, object recognition, tracking, and textual and vocal recognition, and it exhibits excellent performance. Through a deep learning-based lifelog analysis, it is possible to find the life habits or patterns of similar users, which can then provide more diverse and personalized services [7].

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