Single-Item Fashion Recommender: Towards Cross-Domain Recommendations
- URL: http://arxiv.org/abs/2111.00758v1
- Date: Mon, 1 Nov 2021 08:15:31 GMT
- Title: Single-Item Fashion Recommender: Towards Cross-Domain Recommendations
- Authors: Seyed Omid Mohammadi, Hossein Bodaghi, Ahmad Kalhor (University of
Tehran, College of Engineering, School of Electrical and Computer
Engineering, Tehran, Iran)
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Nowadays, recommender systems and search engines play an integral role in
fashion e-commerce. Still, many challenges lie ahead, and this study tries to
tackle some. 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 and make in-shop recommendations by listing similar items
available in the store. Next, the same structure is enhanced to personalize the
results based on user preferences. This work then introduces a background
augmentation technique that makes the system more robust to out-of-domain
queries, enabling it to make street-to-shop recommendations using only a
training set of catalog shop images. Moreover, the last contribution of this
paper is a new evaluation metric for recommendation tasks called
objective-guided human score. This method is an entirely customizable framework
that produces interpretable, comparable scores from subjective evaluations of
human scorers.
Related papers
- Leveraging User-Generated Reviews for Recommender Systems with Dynamic Headers [5.464901224450247]
E-commerce platforms have a vast catalog of items to cater to their customers' shopping interests.
Many models have been proposed in academic literature to generate and enhance the ranking and recall set of items in these carousels.
This work proposes a novel approach to customize the header generation process of these carousels.
arXiv Detail & Related papers (2024-09-11T21:18:21Z) - General Item Representation Learning for Cold-start Content Recommendations [12.729624639270405]
We propose a domain/data-agnostic item representation learning framework for cold-start recommendations.
Our proposed model is end-to-end trainable completely free from classification labels.
arXiv Detail & Related papers (2024-04-22T00:48:56Z) - Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential
Recommendations [50.03560306423678]
We propose Ada-Retrieval, an adaptive multi-round retrieval paradigm for recommender systems.
Ada-Retrieval iteratively refines user representations to better capture potential candidates in the full item space.
arXiv Detail & Related papers (2024-01-12T15:26:40Z) - ItemSage: Learning Product Embeddings for Shopping Recommendations at
Pinterest [60.841761065439414]
At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases.
This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost.
arXiv Detail & Related papers (2022-05-24T02:28:58Z) - A Review on Pushing the Limits of Baseline Recommendation Systems with
the integration of Opinion Mining & Information Retrieval Techniques [0.0]
Recommendation Systems allow users to identify trending items among a community while being timely and relevant to the user's expectations.
Deep Learning methods have been brought forward to achieve better quality recommendations.
Researchers have tried to expand on the capabilities of standard recommendation systems to provide the most effective recommendations.
arXiv Detail & Related papers (2022-05-03T22:13:33Z) - 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) - Buy Me That Look: An Approach for Recommending Similar Fashion Products [5.9707788912142155]
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.
arXiv Detail & Related papers (2020-08-26T16:01:00Z) - 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) - Controllable Multi-Interest Framework for Recommendation [64.30030600415654]
We formalize the recommender system as a sequential recommendation problem.
We propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec.
Our framework has been successfully deployed on the offline Alibaba distributed cloud platform.
arXiv Detail & Related papers (2020-05-19T10:18:43Z) - 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.