생체 감성 정보와 협업 필터링을 이용한 감성 이미지 추천 시스템 설계
Design of Emotion Image Recommendation System using Bio Emotion Information and Collaborative Filtering
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초록
In this paper, we propose an image recommendation system that considers the user's emotions in the proposed system. We measure ECG and PPG, which are biological information of the user, and then use frequency analysis algorithm and SVM-GA algorithm implemented the recommended system and tried to improve the reliability of recommendation system by using collaborative filtering. We implemented a user friendly interface to verify the emotion information of the images through the recommended values and measurement graphs of the emotional images through the mobile application, thereby enhancing the usability of the proposed system. Experimental results showed that subjects' biometric information(ECG, PPG) were classified and learned by SVM-GA algorithm and classified into 4 kinds of emotion information according to biometric information. The average accuracy of the classified data was 89.2%. In addition, 86.7% of the users' satisfaction was measured, suggesting that the proposed emotion based search result is comparable to the emotion felt by a person.
Abstract
In this paper, we propose an image recommendation system that considers the user's emotions in the proposed system. We measure ECG and PPG, which are biological information of the user, and then use frequency analysis algorithm and SVM-GA algorithm implemented the recommended system and tried to improve the reliability of recommendation system by using collaborative filtering. We implemented a user friendly interface to verify the emotion information of the images through the recommended values and measurement graphs of the emotional images through the mobile application, thereby enhancing the usability of the proposed system. Experimental results showed that subjects' biometric information(ECG, PPG) were classified and learned by SVM-GA algorithm and classified into 4 kinds of emotion information according to biometric information. The average accuracy of the classified data was 89.2%. In addition, 86.7% of the users' satisfaction was measured, suggesting that the proposed emotion based search result is comparable to the emotion felt by a person.
참고문헌 (22)
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