Convolutional Neural Network Model Using Data Augmentation for Emotion AI-based Recommendation Systems

• Published in: 한국컴퓨터정보학회논문지 Vol. 28; no. 12; pp. 57 - 66
• Main Authors: Ho-yeon Park(박호연), Kyoung-jae Kim(김경재)
• Format: Journal Article
• Language: English
• Published: 한국컴퓨터정보학회 01.12.2023
• Subjects: 컴퓨터학; 정서적 AI; Emotion AI; 데이터 증강; 추천시스템; 감정분류모형; 합성곱신경망; Convolutional neural network; Emotion classification model; Recommendation systems; Data augmentation
• ISSN: 1598-849X; 2383-9945
• DOI: 10.9708/jksci.2023.28.12.057

In this study, we propose a novel research framework for the recommendation system that can estimate the user's emotional state and reflect it in the recommendation process by applying deep learning techniques and emotion AI (artificial intelligence). To this end, we build an emotion classification model that classifies each of the seven emotions of angry, disgust, fear, happy, sad, surprise, and neutral, respectively, and propose a model that can reflect this result in the recommendation process. However, in the general emotion classification data, the difference in distribution ratio between each label is large, so it may be difficult to expect generalized classification results. In this study, since the number of emotion data such as disgust in emotion image data is often insufficient, correction is made through augmentation. Lastly, we propose a method to reflect the emotion prediction model based on data through image augmentation in the recommendation systems. KCI Citation Count: 0