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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/79578

    Title: 基於風格向量空間之個性化協同過濾服裝推薦系統;Clothing Recommendation System Based on Personalized Collaborative Filtering and Style Vector Space
    Authors: 柳炘妤;Liu, Sin-Yu
    Contributors: 資訊工程學系在職專班
    Keywords: 深度學習;卷積神經網路;推薦系統;服裝風格推薦;Deep Learning;Convolutional Neural Networks;Fashion Recommendation System
    Date: 2018-10-15
    Issue Date: 2019-04-02 15:03:54 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 近年來,透過網路能隨時讓消費者在網路上直接購買各種服飾商品,所帶來的便利性使得網路購物成為人們的主要消費模式,而後續又衍生出穿衣搭配推薦的熱潮。本文針對穿衣搭配目前現有的推薦技術進行了研究,並提出了一種能夠使用在網路購物平台上的符合用戶個性化風格的搭配推薦方法。
    本研究使用深度學習技術與框架,實作一套以風格向量空間為基礎的個人化推薦系統,本系統使用公開的 50 萬張服飾圖片資料集、34 萬筆專家提供的搭配集數據及 1000 萬用戶歷史購買數據,本文的主要內容包括以下幾個方面:首先使用資料庫中多張服飾單品之圖片集合轉換為「特徵向量空間」,再使用此特徵向量空間加上專家推薦集數據訓練出「風格向量空間」,最後一個階段使用餘弦相似度演算法,在訓練後的模型實做個性化推薦系統,在風格向量空間內去找出與用戶風格相近的其他使用者,利用關連使用者的歷史購買紀錄來做個性化的服裝推薦,在最後實驗中,約有 70% 的使用者滿意本系統推薦的結果。茲證明本推薦系統在基於風格的推薦上有一定的成效。
    ;In recent years, customers can buy all kinds of fashion products online at any time. Fashion online shopping becomes more and more popular due to it′s convenience. Therefore, the clothing recommendation system has become more important for those online shopping web- sites as well. We study the exciting fashion recommendation system and proposes a clothing recommendation system based on personalized collaborative filtering and style vector space.
    In this study, we uses deep learning techniques to implement a personalized recommendation system based on style vector space. This system totally uses 500,000 clothing pictures, 340,000 clothing matching data provided by experts and 10 million online shopping history. First, we train the "Feature Vector Space" by multiple clothing pictures. Second, we extend the "Feature Vector Space" to the "Style Vector Space" by using fashion match sets. After that, we can find the users who have similar shopping preference by using cosine similarity in the style vector space. Finally, we can recommend user shopping item based on his/her related users and their shopping history. In our experiment, we find about 70% users are satisfied with our recommendation system.
    Appears in Collections:[資訊工程學系碩士在職專班 ] 博碩士論文

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