Product Review Image Ranking for Fashion E-commerce

• URL: http://arxiv.org/abs/2308.05390v1
• Date: Thu, 10 Aug 2023 07:09:13 GMT
• Title: Product Review Image Ranking for Fashion E-commerce
• Authors: Sangeet Jaiswal, Dhruv Patel, Sreekanth Vempati, Konduru Saiswaroop
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Image Score: Learning and Evaluating Human Preferences for Mercari Search [2.1555050262085027]
  Large Language Models (LLMs) are being actively studied and used for data labelling tasks. We propose a cost-efficient LLM-driven approach for assessing and predicting image quality in e-commerce settings. We show that our LLM-produced labels correlate with user behavior on Mercari.
  arXiv  Detail & Related papers  (2024-08-21T05:30:06Z)
• Behavior Optimized Image Generation [69.9906601767728]
  We propose BoigLLM, which understands both image content and user behavior. We show that BoigLLM outperforms 13x larger models such as GPT-3.5 and GPT-4 in this task. We release BoigBench, a benchmark dataset containing 168 million enterprise tweets with their media, brand names, time of post, and total likes.
  arXiv  Detail & Related papers  (2023-11-18T07:07:38Z)
• Unposed: Unsupervised Pose Estimation based Product Image Recommendations [4.467248776406006]
  We propose a Human Pose Detection based unsupervised method to scan the image set of a product for the missing ones. The unsupervised approach suggests a fair approach to sellers based on product and category irrespective of any biases. We surveyed 200 products manually, a large fraction of which had at least 1 repeated image or missing variant, and sampled 3K products(20K images) of which a significant proportion had scope for adding many image variants.
  arXiv  Detail & Related papers  (2023-01-19T05:02:55Z)
• Visually Similar Products Retrieval for Shopsy [0.0]
  We design a visual search system for reseller commerce using a multi-task learning approach. Our model consists of three different tasks: attribute classification, triplet ranking and variational autoencoder (VAE)
  arXiv  Detail & Related papers  (2022-10-10T10:59:18Z)
• Can you recommend content to creatives instead of final consumers? A RecSys based on user's preferred visual styles [69.69160476215895]
  This report is an extension of the paper "Learning Users' Preferred Visual Styles in an Image Marketplace", presented at ACM RecSys '22. We design a RecSys that learns visual styles preferences to the semantics of the projects users work on.
  arXiv  Detail & Related papers  (2022-08-23T12:11:28Z)
• Exploring CLIP for Assessing the Look and Feel of Images [87.97623543523858]
  We introduce Contrastive Language-Image Pre-training (CLIP) models for assessing both the quality perception (look) and abstract perception (feel) of images in a zero-shot manner. Our results show that CLIP captures meaningful priors that generalize well to different perceptual assessments.
  arXiv  Detail & Related papers  (2022-07-25T17:58:16Z)
• Deep Analysis of Visual Product Reviews [3.120478415450056]
  In the past, the researchers worked on analyzing language feedback, but here we do not take any assistance from linguistic reviews that may be absent. We propose a hierarchical architecture, where the higher-level model engages in product categorization, and the lower-level model pays attention to predicting the review score from a customer-provided product image. The proposed hierarchical architecture attained a 57.48% performance improvement over the single-level best comparable architecture.
  arXiv  Detail & Related papers  (2022-07-19T18:10:43Z)
• User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning [64.07820203919283]
  We propose a novel user-guided personalized image aesthetic assessment framework. It leverages user interactions to retouch and rank images for aesthetic assessment based on deep reinforcement learning (DRL) It generates personalized aesthetic distribution that is more in line with the aesthetic preferences of different users.
  arXiv  Detail & Related papers  (2021-06-14T15:19:48Z)
• PreSizE: Predicting Size in E-Commerce using Transformers [76.33790223551074]
  PreSizE is a novel deep learning framework which utilizes Transformers for accurate size prediction. We demonstrate that PreSizE is capable of achieving superior prediction performance compared to previous state-of-the-art baselines. As a proof of concept, we demonstrate that size predictions made by PreSizE can be effectively integrated into an existing production recommender system.
  arXiv  Detail & Related papers  (2021-05-04T15:23:59Z)
• E-commerce Query-based Generation based on User Review [1.484852576248587]
  We propose a novel seq2seq based text generation model to generate answers to user's question based on reviews posted by previous users. Given a user question and/or target sentiment polarity, we extract aspects of interest and generate an answer that summarizes previous relevant user reviews.
  arXiv  Detail & Related papers  (2020-11-11T04:58:31Z)
• Self-Supervised Ranking for Representation Learning [108.38993212650577]
  We present a new framework for self-supervised representation learning by formulating it as a ranking problem in an image retrieval context. We train a representation encoder by maximizing average precision (AP) for ranking, where random views of an image are considered positively related. In principle, by using a ranking criterion, we eliminate reliance on object-centric curated datasets.
  arXiv  Detail & Related papers  (2020-10-14T17:24:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.