TY - JOUR
T1 - Fashion Recommendations through Cross-media Information Retrieval
AU - Zhou, Wei
AU - Mok, P.Y.
AU - Zhou, Yanghong
AU - Zhou, Yangping
AU - Shen, Jialie
AU - Qu, Qiang
AU - Chau, K.P.
PY - 2019/5/1
Y1 - 2019/5/1
N2 - Fashion recommendation has attracted much attention given its ready applications to e-commerce. Traditional methods usually recommend clothing products to users on the basis of their textual descriptions. Product images, although covering a large resource of information, are often ignored in the recommendation processes. In this study, we propose a novel fashion product recommendation method based on both text and image mining techniques. Our model facilitates two kinds of fashion recommendation, namely, similar product and mix-and-match, by leveraging text-based product attributes and image features. To suggest similar products, we construct a new similarity measure to compare the image colour and texture descriptors. For mix-and-match recommendation, we firstly adopt convolutional neural network (CNN) to classify fine-grained clothing categories and fine-grained clothing attributes from product images. Algorithm is developed to make mix-and-match recommendations by integrating the image extracted categories and attributes information are with text-based product attributes. Our comprehensive experimental work on a real-life online dataset has demonstrated the effectiveness of the proposed method.
AB - Fashion recommendation has attracted much attention given its ready applications to e-commerce. Traditional methods usually recommend clothing products to users on the basis of their textual descriptions. Product images, although covering a large resource of information, are often ignored in the recommendation processes. In this study, we propose a novel fashion product recommendation method based on both text and image mining techniques. Our model facilitates two kinds of fashion recommendation, namely, similar product and mix-and-match, by leveraging text-based product attributes and image features. To suggest similar products, we construct a new similarity measure to compare the image colour and texture descriptors. For mix-and-match recommendation, we firstly adopt convolutional neural network (CNN) to classify fine-grained clothing categories and fine-grained clothing attributes from product images. Algorithm is developed to make mix-and-match recommendations by integrating the image extracted categories and attributes information are with text-based product attributes. Our comprehensive experimental work on a real-life online dataset has demonstrated the effectiveness of the proposed method.
U2 - 10.1016/j.jvcir.2019.03.003
DO - 10.1016/j.jvcir.2019.03.003
M3 - Article
VL - 61
SP - 112
EP - 120
JO - Journal of Visual Communication and Image Representation
JF - Journal of Visual Communication and Image Representation
SN - 1047-3203
ER -