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Fashion recommendations through cross-media information retrieval.
- Source :
-
Journal of Visual Communication & Image Representation . May2019, Vol. 61, p112-120. 9p. - Publication Year :
- 2019
-
Abstract
- • A novel fashion product recommendation method using multimodal information. • Integrating both text and image mining techniques. • Facilitating similar and mix-and-match fashion product recommendations. • Leveraging text-based product attributes and image features in the recommendations. 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10473203
- Volume :
- 61
- Database :
- Academic Search Index
- Journal :
- Journal of Visual Communication & Image Representation
- Publication Type :
- Academic Journal
- Accession number :
- 136156526
- Full Text :
- https://doi.org/10.1016/j.jvcir.2019.03.003