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Have you ever looked at an Instagram model, or a model in a fashione-commerce web-page, and thought \textit{"Wish I could get a list of fashionitems similar to the ones worn by the model!"}. This is what we address in thispaper, where we propose a novel computer vision based technique called\textbf{ShopLook} to address the challenging problem of recommending similarfashion products. The proposed method has been evaluated at Myntra(www.myntra.com), a leading online fashion e-commerce platform. In particular,given a user query and the corresponding Product Display Page (PDP) against thequery, the goal of our method is to recommend similar fashion productscorresponding to the entire set of fashion articles worn by a model in the PDPfull-shot image (the one showing the entire model from head to toe). Thenovelty and strength of our method lies in its capability to recommend similararticles for all the fashion items worn by the model, in addition to theprimary article corresponding to the query. This is not only important topromote cross-sells for boosting revenue, but also for improving customerexperience and engagement. In addition, our approach is also capable ofrecommending similar products for User Generated Content (UGC), eg., fashionarticle images uploaded by users. Formally, our proposed method consists of thefollowing components (in the same order): i) Human keypoint detection, ii) Poseclassification, iii) Article localisation and object detection, along withactive learning feedback, and iv) Triplet network based image embedding model.

Buy Me That Look: An Approach for Recommending Similar Fashion Products