Buy Me That Look: An Approach for Recommending Similar Fashion Products
- URL: http://arxiv.org/abs/2008.11638v2
- Date: Tue, 6 Apr 2021 13:05:30 GMT
- Title: Buy Me That Look: An Approach for Recommending Similar Fashion Products
- Authors: Abhinav Ravi, Sandeep Repakula, Ujjal Kr Dutta, Maulik Parmar
- Abstract summary: We propose a novel computer vision based technique called textbfShopLook' to address the problem of recommending similar fashion products.
The proposed method has been evaluated at Myntra, a leading online fashion e-commerce platform.
- Score: 5.9707788912142155
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- AI Recommendation System for Enhanced Customer Experience: A Novel
Image-to-Text Method [2.2975420753582028]
This research describes an innovative end-to-end pipeline that uses artificial intelligence to provide fine-grained visual interpretation for fashion recommendations.
When customers upload images of desired products or outfits, the system automatically generates meaningful descriptions emphasizing stylistic elements.
arXiv Detail & Related papers (2023-11-16T07:15:44Z) - Social Media Fashion Knowledge Extraction as Captioning [61.41631195195498]
We study the task of social media fashion knowledge extraction.
We transform the fashion knowledges into a natural language caption with a sentence transformation method.
Our framework then aims to generate the sentence-based fashion knowledge directly from the social media post.
arXiv Detail & Related papers (2023-09-28T09:07:48Z) - Unified Vision-Language Representation Modeling for E-Commerce
Same-Style Products Retrieval [12.588713044749177]
Same-style products retrieval plays an important role in e-commerce platforms.
We propose a unified vision-language modeling method for e-commerce same-style products retrieval.
It is capable of cross-modal product-to-product retrieval, as well as style transfer and user-interactive search.
arXiv Detail & Related papers (2023-02-10T07:24:23Z) - FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified
Retrieval and Captioning [66.38951790650887]
Multimodal tasks in the fashion domain have significant potential for e-commerce.
We propose a novel fashion-specific pre-training framework based on weakly-supervised triplets constructed from fashion image-text pairs.
We show the triplet-based tasks are an effective addition to standard multimodal pre-training tasks.
arXiv Detail & Related papers (2022-10-26T21:01:19Z) - Fashionformer: A simple, Effective and Unified Baseline for Human
Fashion Segmentation and Recognition [80.74495836502919]
In this work, we focus on joint human fashion segmentation and attribute recognition.
We introduce the object query for segmentation and the attribute query for attribute prediction.
For attribute stream, we design a novel Multi-Layer Rendering module to explore more fine-grained features.
arXiv Detail & Related papers (2022-04-10T11:11:10Z) - Single-Item Fashion Recommender: Towards Cross-Domain Recommendations [0.0]
This article first suggests a content-based fashion recommender system that uses a parallel neural network to take a single fashion item shop image as input.
Next, the same structure is enhanced to personalize the results based on user preferences.
The last contribution of this paper is a new evaluation metric for recommendation tasks called objective-guided human score.
arXiv Detail & Related papers (2021-11-01T08:15:31Z) - Addressing the Cold-Start Problem in Outfit Recommendation Using Visual
Preference Modelling [51.147871738838305]
This paper attempts to address the cold-start problem for new users by leveraging a novel visual preference modelling approach.
We demonstrate the use of our approach with feature-weighted clustering to personalise occasion-oriented outfit recommendation.
arXiv Detail & Related papers (2020-08-04T10:07:09Z) - Personalized Fashion Recommendation from Personal Social Media Data: An
Item-to-Set Metric Learning Approach [71.63618051547144]
We study the problem of personalized fashion recommendation from social media data.
We present an item-to-set metric learning framework that learns to compute the similarity between a set of historical fashion items of a user to a new fashion item.
To validate the effectiveness of our approach, we collect a real-world social media dataset.
arXiv Detail & Related papers (2020-05-25T23:24:24Z) - Fashion Recommendation and Compatibility Prediction Using Relational
Network [18.13692056232815]
We develop a Relation Network (RN) to develop new compatibility learning models.
FashionRN learns the compatibility of an entire outfit, with an arbitrary number of items, in an arbitrary order.
We evaluate our model using a large dataset of 49,740 outfits that we collected from Polyvore website.
arXiv Detail & Related papers (2020-05-13T21:00:54Z) - Learning Diverse Fashion Collocation by Neural Graph Filtering [78.9188246136867]
We propose a novel fashion collocation framework, Neural Graph Filtering, that models a flexible set of fashion items via a graph neural network.
By applying symmetric operations on the edge vectors, this framework allows varying numbers of inputs/outputs and is invariant to their ordering.
We evaluate the proposed approach on three popular benchmarks, the Polyvore dataset, the Polyvore-D dataset, and our reorganized Amazon Fashion dataset.
arXiv Detail & Related papers (2020-03-11T16:17:08Z)
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.