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데이터 임베딩을 활용한 사용자 플레이리스트 기반 음악 추천에 관한 연구
이현수(Hyeonsu Lee),홍성은(Seongeun Hong),방준일(Junil Bang),김화종(Hwajong Kim) 한국정보기술학회 2020 한국정보기술학회논문지 Vol.18 No.9
Recently, the online recommendation system, which is attracting attention, analyzes many variables such as user behavior pattern, item characteristics, and additional variables to recommend items that users want. In this paper, we propose a new method to recommend each item through data embedding and clustering using various catalog formats in the Melon music data set. The proposed method of recommending music based on user playlist using data embedding is used for learning by converting information about songs such as tags, genres, detailed genres, and singer names into a sentence form combined a list of words. The comparison performance evaluation and Item2Vec method of the proposed method are performed based on the similarity of embedded songs by embedding songs in multidimensional vector space through SGNS. As a result, the proposed method improved the recommended performance with an average nDCG 0.2996, compared to the average nDCG 0.1850 of Item2Vec.
클래스 불균형 문제에 연합학습 적용을 위한 최적화 기법 연구
이현수(Hyeonsu Lee),홍성은(Seongeun Hong),방준일(Junil Bang),김화종(Hwajong Kim) 한국정보기술학회 2021 한국정보기술학회논문지 Vol.19 No.1
Recently, as highly advanced personal identification technology has made it easier to identify individuals, various measures are required to guarantee the rights of information subjects in the information society. Federated learning is a machine learning approach proposed by these needs, a specific approach to educating machine learning algorithms while keeping the data private. In this paper, in order to identify problems that may arise when applying federated learning to the medical industry, which is sensitive to privacy issues, a retinal patient data set, was disproportionately distributed like the environment in which the actual medical institution holds the data. As a result of experiments applying various learning optimization techniques to class imbalance problems that occur here, F1 score 0.96 was achieved in experiments with under sampling and TopkAvg techniques, and the learning time was also shortened.