基於色彩學改善個人化服裝推薦

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姓名

李紹齊(Shao-Chi Lee) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

基於色彩學改善個人化服裝推薦
(Application of Chromatics on Personalized Clothing Recommendation)

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至系統瀏覽論文 (2028-7-12以後開放)

摘要(中)

服裝推薦問題是近年諸多學者研究的議題，尤其是個人化服裝推薦任務有大
量方法進行探討，其主要結合商品圖像、文字特徵，以及用戶評分、評論等。本
研究針對過往在服裝推薦中鮮少討論的色彩學，以及使用者美學偏好作為基礎，
建立個人化的線上服裝推薦系統。
過去研究將色彩視為單純圖片特徵，缺乏消費者對其色彩學構成的心理因素，而本研究討論服裝圖片的色彩學帶給消費者美學概念，以達成個人化美學偏好推薦，並增加推薦合理性。本研究以 OB 嚴選具設計風格的穿搭圖片進行整體色彩、服裝色彩、單品色彩風格提取，其依據為使用者會受圖片中不同元素吸引，而對其有不同的美學印象及偏好。
研究架構分為內容導向、模型式協同過濾、混合式推薦方法。內容導向推薦討論三大風格色彩如何影響商品間相似度，協同過濾推薦將使用者進行美學偏好分群，再推薦與其同偏好使用者的商品選擇，混合式推薦則先將使用者進行偏好分群，再依據其推薦候選找出與目標商品相似度高的服裝商品，最後評估推薦方法間的 Hit Ratio。結果發現混合式及基於分群模型的推薦方法效果較好，驗證了色彩學及美學概念應用於使用者偏好建模及服裝推薦的可行性及合理性。

摘要(英)

Clothing recommendation has always been a popular topic, especially for clothing personalization. Generally, clothing recommendation requires images or texts information, and comments or ratings as well. Our study focuses on the chromatics features on images, which is seldom discussed by previous studies. We aim to build an online personalized clothing recommendation system based on users’ aesthetic preference.
Colors were regarded as just simple features of images by previous studies, which lacked a user’s psychological factors affected by images’ chromatics. Therefore, our study discusses the aesthetic concepts that images’ chromatics brings to users and build a personalized aesthetic recommendation with higher interpretability.
Users tend to be attracted by elements in images, so we thus extract the overall, clothing, and single-color style from OB Design’s clothing dataset to build users’ aesthetic preference. There are three recommendation systems, which include content- based, model-based collaborative filtering, and hybrid methods. To elaborate, content- based method is for computation on products similarity based on chromatics, model- based collaborative filtering is to cluster user’s aesthetic preference, and hybrid method uses both methods.
We conduct three experiments and assess the results by Hit Ratio, and we find the third system using both methods perform the best among all systems. In conclusion, our methods prove that applying chromatics and aesthetic features to model users’ preference and recommend clothing items is feasible and reasonable.

關鍵字(中)

★ 服裝推薦
★ 色彩美學特徵
★ 服裝物件擷取

關鍵字(英)

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 5
第二章文獻探討 7
2.1 服裝色彩學與推薦 7
第三章研究方法 11
3.1 資料蒐集 12
3.2 資料處理 14
3.2.1 商品資料前處理—服裝物件辨識 14
3.2.2 商品資料前處理—三大風格色彩擷取 16
3.2.3 使用者資料前處理—服裝物件辨識、三大風格色彩擷取、美學詞彙轉換 18
3.3 尋找 Top-k 鄰居商品 20
3.3.1 商品—色彩特徵矩陣 20
3.3.2 計算商品相似度 22
3.4 尋找相似使用者 24
3.4.1 使用者美學特徵矩陣 24
3.4.2 找出相似使用者 24
3.5 推薦方法 25
3.5.1 推薦系統一：內容導向推薦 25
3.5.2 推薦系統二：基於模型式分群模型之協同過濾推薦 26
3.5.3 推薦系統三：結合模型式分群協同過濾及內容導向推薦 26
3.6 實驗設計 26
3.7 實驗評估指標 28
第四章實驗結果與分析 29
4.1 實驗資料 29
4.2 實驗結果評估 29
4.2.1 實驗一、調整相似度計算權重wp、wc、ws的比例 29
4.2.2 實驗二、調整至少購買i個商品數量 30
4.2.3 實驗三、調整不同Top-k推薦結果 31
第五章研究結論與建議 33
5.1 研究結論 33
5.2 研究限制 34
5.3 未來研究方向與建議 35
參考文獻 36

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指導教授

胡雅涵(Ya-Han Hu)

審核日期

2023-7-18

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