臺灣博碩士論文加值系統

隨著網路購物的興起，流行服飾產業以網路行銷的方式蓬勃發展，許多研究開始致力於服飾類商品的推薦，幫助使用者挑選適當的商品，省去在龐大商品之海中茫然選擇的時間與精力，提供便利又迅速的方式，讓使用者獲取時髦好看的穿搭樣式。而針對服裝之間的搭配性，與大眾流行的元素，作為網路購物的商品推薦，不僅讓平時不善於流行穿搭的人，取得符合當今潮流趨勢且恰當的服飾搭配建議，藉由推薦可相互搭配的商品，更能同時提升商品的連帶銷售率，以增加商家的獲利。
本研究旨在提出一套基於風格與熱門度的服飾搭配推薦機制，並且實作出系統。首先，考量整體風格對於搭配關係的影響，從大量服飾資料，擷取套裝(Outfit)中所有單品(Item)的文字資訊，包含商品標題、內容描述、關鍵詞等文字詞袋，以Latent Dirichlet Allocation (LDA)進行Topic Modeling，計算每組套裝的主題分佈，將最具代表性的主題視為其套裝所屬之風格。另外，為了使系統有效判別輸入圖片的商品類別，再依照使用者所選風格，推薦相異類別且彼此適合搭配的單品，因此，利用Convolutional Neural Network (CNN)訓練兩種影像分類模型。其一是單品類別分類模型；其二，在學習服飾影像上的搭配概念時，根據套裝單品的影像內容和喜好分數，以熱門程度作為衡量搭配性的標準，建立套裝搭配分類模型，學習商品之間是否適合搭配的潛在規則。
最終開發一服飾搭配的網站系統，提供平台讓使用者選取喜歡的風格、上傳服飾圖片，由系統自動為圖片挑選在相同風格、相異類別中，最適宜搭配的服飾產品予以推薦。

With the rise of online shopping, the fashion industry has flourished by the mode of internet marketing. Much research has begun to focus on the recommendation of apparel products, helping users to select appropriate items for saving the time and effort in choosing from a huge amount of products, which provides a convenient and fast way for users to get a stylish and good-looking dressing patterns. By aiming at the clothing compatibilities and popular elements as factors of fashion product recommendation, to recommend items that can be matched with each other for online shopping, not only enables people who are not good at wearing to obtain the suitable clothing matching suggestions with today’s fashion trend, but also can increase the profits of business while improving joint sales rate of goods.
This thesis proposes a clothing matching recommendation mechanism based on style and popularity, and implements the clothing recommendation system. First of all, we consider the influence of overall style on the collocation relationships. From a large number of fashion data, we capture the text information of all items in outfits, including the product title, category, description. Then, Latent Dirichlet Allocation (LDA) topic model is employed to calculate the theme distribution for each outfit, and the most representative theme is manually defined as the style of the outfit. In addition, in order to allow the system to effectively discriminate the item category of the input image, and to recommend matching items in heterogeneous categories according the style selected by the user, therefore, we use Convolutional Neural Network (CNN) to train two image classification models. The first one is the category classification model which can recognize the image is top or others. The second one is outfit compatibility classification model which automatically learn whether the item pairs are suitable matching between each other or not. When learning the concept of matching on item images, we utilize the image contents and user preference scores that indicate the popularity of the product as a measure of clothing compatibility.
Finally, we develop a web system for clothing matching recommendation, that provides a platform for users to select the style they like and upload the clothing image. This system can automatically recommend users the suitable clothing products, that are matching with input image in the same style and different categories.

第一章 緒論 1
1.1研究背景、動機與目的 1
1.2論文貢獻 4
1.3論文架構 5
第二章 相關研究 6
2.1服裝解析與識別 6
2.2服裝檢索 7
2.3服裝推薦 8
2.3.1單品推薦 9
2.3.2套裝推薦 10
2.4時尚趨勢分析與預測 10
第三章 研究方法 12
3.1系統架構 12
3.2 Outfit Style Recognition 14
3.2.1 Bag-of-Word (BOW) 14
3.2.2 Latent Dirichlet Allocation (LDA) 16
3.3服飾推薦 19
3.3.1 Convolutional Neural Network (CNN) 19
3.3.2 Item Category Recognition 23
3.3.3 Outfit Compatibility Prediction 24
第四章 系統實作 26
4.1 Web系統架構 26
4.2開發框架與工具 27
4.2.1 Django 27
4.2.2 D3.js 28
4.2.3 PostgreSQL 28
4.3系統介面設計 29
第五章 實驗 32
5.1資料搜集 32
5.2資料前處理 36
5.3實驗設計與評估 37
5.3.1分群模型評估 37
5.3.2分類模型評估 38
5.4實驗結果 39
5.4.1套裝風格分群結果 40
5.4.2單品類別分類結果 44
5.4.3套裝搭配分類結果 45
第六章 結論與未來研究 46
參考文獻 47

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