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Title:	日本流行雜誌封面人物偏好分析 Analysis of the preference of Japanese popular magazine cover character
Authors:	楊宇晴 Yang, Yu-Ching
Contributors:	羅崇銘 Lo, Chung-Ming 楊宇晴 Yang, Yu-Ching
Keywords:	日本流行雜誌封面人物亞馬遜人臉辨識系統銷售量預測機器學習深度學習 Japanese fashion magazines Cover character AWS Rekognition Sales prediction Machine learning Deep learning
Date:	2024
Issue Date:	2024-02-01 12:52:53 (UTC+8)
Abstract:	日本的流行雜誌於1970年代發展逐漸趨近於成熟，現今與日本人的日常生活密不可分。而流行雜誌的封面人物正是代表該本雜誌的品味以及該期出版的主題重心，更具有向潛在讀者介紹內容或宣傳內頁故事的功能。另外更能反映出日本當代社會對於特定人物或潮流的關注度和興趣，也可以呈現出對某些特定議題或價值觀的強調。本研究搜集80本不同類型的雜誌，總共包含5,604張封面影像。透過分析雜誌本身的6個屬性特徵，以及運用AWS Rekognition技術對封面人物的10個臉部特徵進行分析。研究結果顯示，MAGAZINE HOUSE是高銷售雜誌的主要出版社，讀者偏好月刊發行頻率，風格類型以時尚和生活方式雜誌為主，性別類型偏好女性類別雜誌，年齡類型則偏好青壯年、青少年和中年雜誌。在封面人物的呈現上，讀者偏好展現歡樂和驚訝的表情，更喜歡微笑、無眼鏡&墨鏡、無遮擋的封面人物。此外，本研究亦透過機器學習模型預測銷售量，結果顯示除了雜誌本身的屬性外，封面人物的外貌和表情等特徵都能對銷售表現產生影響。並在使用全部16個屬性特徵進行預測時，隨機森林分類器的準確率達到96.34%。建議雜誌出版商持續優化封面設計，確保充分利用這些關鍵因素，提升雜誌的吸引力，取得更好的銷售成績。 The development of popular magazines in Japan gradually matured in the 1970s and is now inseparable from the daily lives of the Japanese. The cover characters of these magazines represent the taste of the publication and the thematic focus of each issue, serving as a means to introduce content or promote inner stories to potential readers. Moreover, they reflect the contemporary Japanese society's attention and interest in specific figures or trends, emphasizing certain issues or values. This research collected 80 magazines of different genres, totaling 5,604 cover images. Through analyzing six attributes of the magazines and utilizing AWS Rekognition technology to analyze ten facial features of the cover characters, the research results indicate that MAGAZINE HOUSE is the main publisher of high-selling magazines. Readers prefer monthly publication frequency. The style type is mainly fashion and lifestyle magazines. The gender type prefers women's magazines. The age type prefers young adults, teenagers and middle-aged magazines. In terms of cover characters, readers prefer expressions of happy and surprise, and they prefer cover characters with smiles, no glasses & sunglasses, and no obstruction. Furthermore, the research used a machine learning model to predict sales, demonstrating that besides magazine attributes, the appearance and expressions of cover characters significantly impact sales performance. When using all 16 attribute features for prediction, the random forest classifier achieved an accuracy rate of 96.34%. The research recommends continuous optimization of cover designs by magazine publishers, ensuring the effective utilization of these key factors to enhance attractiveness and achieve better sales outcomes.
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Description:	碩士國立政治大學圖書資訊學數位碩士在職專班 110913003
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