| Summary: | 碩士 === 國立中央大學 === 資訊工程學系在職專班 === 107 === 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.
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