Fashion recommendations through cross-media information retrieval

Wei Zhou, P. Y. Mok, Yanghong Zhou, Yangping Zhou, Jialie Shen, Qiang Qu, K. P. Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

28 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)112-120
Number of pages9
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - 1 May 2019


  • Fashion recommendations
  • Human parsing
  • Image features
  • Image retrieval

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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