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

TL;DR

This paper introduces ShopLook, a computer vision technique for recommending similar fashion products worn by models or uploaded by users, enhancing cross-selling and customer engagement on e-commerce platforms.

Contribution

The paper presents a novel multi-component approach combining human pose detection, object localization, and triplet network embeddings for comprehensive fashion product recommendation.

Findings

Effective in recommending similar items for all fashion articles worn by models.

Capable of recommending similar products for user-uploaded images.

Evaluated successfully on Myntra, a leading fashion e-commerce platform.

Abstract

Have you ever looked at an Instagram model, or a model in a fashion e-commerce web-page, and thought \textit{"Wish I could get a list of fashion items similar to the ones worn by the model!"}. This is what we address in this paper, where we propose a novel computer vision based technique called \textbf{ShopLook} to address the challenging problem of recommending similar fashion 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 the query, the goal of our method is to recommend similar fashion products corresponding to the entire set of fashion articles worn by a model in the PDP full-shot image (the one showing the entire model from head to toe). The novelty and strength of our method lies in its capability to recommend similar articles for all the fashion items worn by the model, in addition to the primary article corresponding to the query. This is not only important to promote cross-sells for boosting revenue, but also for improving customer experience and engagement. In addition, our approach is also capable of recommending similar products for User Generated Content (UGC), eg., fashion article images uploaded by users. Formally, our proposed method consists of the following components (in the same order): i) Human keypoint detection, ii) Pose classification, iii) Article localisation and object detection, along with active learning feedback, and iv) Triplet network based image embedding model.