Abstract
The recent proliferation of numerous fashion e-commerce platforms has led to a surge in online shopping of fashion products. Fashion being the dominant aspect in online retail sales, demands for efficient and effective fashion products recommendation systems that could boost revenue, improve customer experience and engagement. In this paper, we focus on the problem of similar fashion item recommendation for multiple fashion items. Given a Product Display Page for a fashion item in an online e-commerce platform, we identify the images with a full-shot look, i.e., the one with a full human model wearing the fashion item. While the majority of existing works in this domain focus on retrieving similar products corresponding to a single item present in a query, we focus on the retrieval of multiple fashion items at once. This is an important problem because while a user might have searched for a particular primary article type (e.g., men's shorts), the human model in the full-shot look image would usually be wearing secondary fashion items as well (e.g., t-shirts, shoes etc). Upon looking at the full-shot look image in the PDP, the user might also be interested in viewing similar items for the secondary article types. To address this need, we use human keypoint detection to first identify the fullshot images, from which we subsequently select the front facing ones. An article detection and localisation module pretrained on a large-dataset is then used to identify different articles in the image. The detected articles and the catalog database images are then represented in a common embedding space, for the purpose of similarity based retrieval. We make use of a triplet-based neural network to obtain the embeddings. Our embedding network by virtue of an active-learning component achieves further improvements in the retrieval performance.
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URL
https://arxiv.org/abs/2008.11638
