AI-powered personalization in E-commerce and Fashion industries: use cases and enable technologies

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what is personalization in E-commerce & fashion?

Well, if we use one short sentence to explain what is personalization for E-commerce and Fashion, it could be like this: customers expect brands and retailers to recognize them and treat each of them as a VIP while shopping online.

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How AI boosts personalization in the E-commerce and Fashion industries?

From text search to image search, interest discovery, then content-based recommendations and virtual fitting, AI technologies play key roles in personalization in E-commerce and Fashion.

Text search

Text search is a kind of early-stage technology. When people use the browser and online app shopping software, sometimes they do not know which specific clothing to choose, and usually, input text for retrieval according to their own needs.

Visual search

As we mentioned early, people can purchase a product based on a real-life photo or screenshot. Visual search is one of the simplest ways for shoppers to find what they’re looking for and shorten their path to purchase.

Fashion Image Caption

As there are a large number of clothing pictures on the Internet, it would be a huge project to label out the characteristics of clothing pictures one by one manually. If we can automatically generate some descriptions of pictures through deep learning, it will greatly improve efficiency.

Content-based Recommendation

Shopping apps often need to recommend users’ preferences, classify them automatically according to the pictures they browse, and then recommend similar clothes to users to promote consumption.

The enabling technologies make things happen

1. Image Classification

Through the image classification technology, the clothing such as: skirts, short sleeves, hats, shorts and other categories are automatically classified. The corresponding application scenarios of image classification are: automatic clothing sorting, content recommendation attribute classification, etc. Classification technology can also be divided into: classification based on clothing style, classification based on clothing attributes.


2. Image segmentation

For a fashion picture, the image segmentation technology can distinguish the clothes, pants, and various accessories of the figure in the picture at the pixel level. In the virtual fitting scene, it is often necessary to identify and segment the clothes worn by the experiencer, so as to replace the virtual clothes more accurately.

Mask R-CNN ,from[1]

3. Cross-Modal Retrieval And Generation

Cross-modal technology in the Fashion field can be divided into two aspects: generating text descriptions through images, and retrieving relevant clothing pictures from massive data through descriptive text (cross-modal retrieval). Cross-modal techniques often require massive image text descriptions and large-scale training of paired images. The commonly used framework of this kind of task is: by extracting image side features and text side features and optimizing the spatial distance of related modes in the same subspace, the spatial distance of unrelated modal features is gradually approaching, and the irrelevant modal features are gradually estranged.


4. Image Generation

Image generation can be divided into conditional generation and unconditional generation. Conditional generation provides examples of clothing to generate images of similar styles, represented by pix2PIx models. Random disturbance is used as input to generate more diversified images such as DCGAN and ProGAN.

Image Generation from

5. Detection

Detection can be divided into target detection and key point detection, and both of them are coordinate points that need to regression clothing according to the picture. It has great technical value in clothing search and recognition.


The Role of Data in E-commerce and Fashion Personalization

What’s at the core of these complex AI technologies?

  1. He, Kaiming, et al. “Mask r-cnn.” Proceedings of the IEEE international conference on computer vision. 2017.
  2. Li, Xirong, et al. “W2vv++ fully deep learning for ad-hoc video search.” Proceedings of the 27th ACM International Conference on Multimedia. 2019.
  3. Redmon, Joseph, et al. “You only look once: Unified, real-time object detection.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
  4. He, Kaiming, et al. “Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.



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