In a fashion e-commerce platform where customers can't physically examine the products on their own, being able to see other customers' text and image reviews of the product is critical while making purchase decisions. Given the high reliance on these reviews, over the years we have observed customers proactively sharing their reviews. With an increase in the coverage of User Generated Content (UGC), there has been a corresponding increase in the number of customer images. It is thus imperative to display the most relevant images on top as it may influence users' online shopping choices and behavior. In this paper, we propose a simple yet effective training procedure for ranking customer images. We created a dataset consisting of Myntra (A Major Indian Fashion e-commerce company) studio posts and highly engaged (upvotes/downvotes) UGC images as our starting point and used selected distortion techniques on the images of the above dataset to bring their quality at par with those of bad UGC images. We train our network to rank bad-quality images lower than high-quality ones. Our proposed method outperforms the baseline models on two metrics, namely correlation coefficient, and accuracy, by substantial margins.

翻译：在时尚电商平台中，顾客无法亲身体验产品，因此查看其他顾客对产品的文字评论和图像评论，对于其做出购买决策至关重要。鉴于顾客对这些评论的高度依赖，多年来我们观察到顾客主动分享评论的行为。随着用户生成内容覆盖范围的扩大，顾客图像数量也相应增加。因此，将最相关的图像置顶显示变得不可或缺，因为这可能影响用户的在线购物选择与行为。本文提出了一种简单而有效的训练流程，用于对顾客图像进行排序。我们以Myntra（印度主要时尚电商公司）工作室帖子及高互动率（点赞/点踩）用户生成图像为基础构建数据集，并对上述数据集中的图像采用选定的失真技术，使其质量与低质量用户生成图像相当。我们训练网络将低质量图像的排序置于高质量图像之后。所提方法在相关系数和准确率两项指标上均大幅超越基线模型。