ASOS improves fashion product recommendations by learning from unstructured data.
problem Lack of consistent product information in e-commerce.
method Developed a hybrid recommender system to learn product attributes from unstructured data.
result Quantitative understanding of products improves recommendation accuracy.
Fashion-MNIST replaces MNIST for machine learning benchmarks.
problem No new problem introduced.
method No new method introduced.
result Fashion-MNIST serves as a direct replacement for MNIST.
A new model predicts fashion demand 6-12 months ahead, boosting retailer profits.
problem Accurate demand forecasting for fashion retailers with short product life cycles.
method Product age-based forecast model, incorporating unique feature engineering.
result Significant revenue uplift of 41% compared to retailer's plan.
The paper proposes a machine learning technique to optimize prices in fashion e-commerce.
problem Optimizing prices for millions of products in fashion e-commerce to maximize revenue and profit.
method Demand prediction, price elasticity, multiple price demand pairs, linear programming optimization.
result The model improved revenue by 1% and gross margin by 0.81% in AB tests.
This study examines how fashion consumption affects self-confidence and buying behavior in Iranian consumers.
problem Understanding the role of self-confidence in fashion buying behavior.
method A questionnaire was used to collect data from 400 consumers in Tehran's clothing market. Structural equations and factor analysis were employed to test the model.
result Interest in fashion, personal taste, utilitarianism, and new products positively impact self-confidence, and self-confidence positively impacts fashion buying behavior.
Deep neural network predicts product returns before purchase.
problem High costs of handling returned fashion products.
method Bayesian Personalized Ranking (BPR) embeddings and skip-gram model for user and product features.
result Reduced overall returns through real-time return probability prediction.
Automates size normalization for fashion items.
problem Reduce merchandise returns in e-commerce.
method Uses sales data to automate size mapping.
result Automated size mappings comparable to human-generated ones.
The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, r…
This work aims to separate buying preferences from merchandise commercials in fashion e-retail.
problem Difficult to infer customer preference from sales data due to implicit signals.
method Extends earlier work on explicit signals to implicit signals from user behavior.
result Derives a metric to separate buying preferences from merchandise commercials.
New data set and method for BTO supply chain demand forecasting.
problem Lack of demand forecasting methods for BTO supply chains.
method Proposes a novel data transformation technique for BTO products.
result Approach compares well to state-of-the-art methods and is easy to implement.
Bayesian model predicts fashion size recommendations and returns.
problem Predicting optimal fashion sizes and handling returns efficiently.
method Hierarchical Bayesian model for size and return events.
result Model incorporates domain expertise and article characteristics.
Method retrieves similar fashion items from images and text, enabling style refinement.
problem Lack of intuitive, interactive refinement in search engines for fashion items.
method Joint visual-textual embedding training, Mini-Batch Match Retrieval, attribute extraction.
result Improved performance in multimodal style search, demonstrated through benchmark.
Paper builds LETOR models for e-commerce, segmenting queries and optimizing search results.
problem Optimizing search results for e-commerce platforms.
method Segmenting queries into broad and narrow, using denoising auto-encoders and skip-gram embeddings, employing various feature types.
result Specialized models for broad and narrow queries outperform a combined model.
ProductNet curates high-quality product datasets for better product understanding.
problem Lack of high-quality product datasets for product representation learning.
method Curated high-quality product datasets with a multi-modal deep neural network and active learning.
result Master model yields high categorization accuracy (94.7% top-1 accuracy for 1240 classes).
Proposes PKG embedding for e-commerce products.
problem Learning product intrinsic relations for e-commerce applications.
method Self-attention-enhanced distributed representation learning model from raw data.
result Compared favorably to baselines in knowledge completion and downstream tasks.
New method uses random discriminators to train GANs more efficiently.
problem Difficulty in training Generative Adversarial Networks (GANs).
method Proposes a new generative network using random discriminators.
result The method leads to a more stable and efficient optimization problem.
We present the Integrated Size and Price Optimization Problem (ISPO) for a fashion discounter with many branches. Based on a two-stage stochastic programming model with recourse, we develop an exact algorithm and a production-compliant heuristic that produces small optimality gaps. In a field study we show that a distr…
Paper tackles product categorization with structured and unstructured attributes for large-scale eCommerce.
problem Challenges in categorizing products with thousands of classes and millions of products.
method Compares hierarchical and flat models, uses Deep Learning for feature extraction, combines structured and unstructured attributes.
result Flat models perform better in specific cases, and the proposed approach handles faulty attribute names and values.
Proposes balancing revenue and environmental impact in assortment planning.
problem Maximizing revenue while considering environmental impact in retail assortment planning.
method Multi-objective optimization using Higg Material Sustainability Index.
result Shows it's possible to have lower environmental impact without significant revenue loss.
TensorNetwork boosts image classification accuracy.
problem Improving image classification accuracy.
method Encoding images into matrix product states and using tensor network contraction.
result 98% and 88% accuracy on MNIST and Fashion-MNIST datasets respectively.
Analyzes fashion demand to optimize style mix in retail.
problem Optimizing fashion assortment planning in retail.
method Derives a style quotient from customer demand data, decoupling style from price.
result Shows the relationship between customer perception and retailer's style mix.
A new object detector identifies fashion items from social media photos.
problem Difficult to parse and classify fashion items from social media content.
method Pretrained unsupervised object detector on 24 categories from Open Images V4.
result 72.7% mAP on test dataset of 2.4K photos, outperforming state-of-the-art.
Fashion-Gen dataset offers high-res fashion images and descriptions for research.
problem Lack of high-quality, detailed fashion image datasets for research.
method Created a large dataset of fashion images and descriptions, provided baseline results, and launched a challenge.
result Baseline results on image generation and conditioning on text descriptions.
A conservative discretization of incompressible Navier-Stokes equations is developed based on discrete exterior calculus (DEC). A distinguishing feature of our method is the use of an algebraic discretization of the interior product operator and a combinatorial discretization of the wedge product. The governing equatio…
This paper removes near-duplicates from Fashion-MNIST to improve testing accuracy.
problem Near-duplicate images in Fashion-MNIST increase testing accuracy, reducing dataset quality.
method Identified and removed near-duplicate images between training and testing sets.
result Improved dataset quality for better testing accuracy in machine learning models.
Robotic clothing manipulation improved with fashion image analysis techniques.
problem Automated identification of clothing categories and landmarks for robotic tasks.
method Training data augmentation methods and rotation invariant convolutions.
result Our approach outperforms state-of-the-art models on unseen datasets.
HERMES model predicts nonstationary fashion trends using social media data.
problem Forecasting nonstationary fashion time series for optimal inventory decisions.
method Hybrid model combining parametric models, seasonal components, and recurrent neural networks with external signals.
result State-of-the-art results on fashion dataset and M4 competition time series.
Paper generates high-resolution fashion images based on body pose.
problem Limited variety of outfit images for shopping.
method Generative model trained on body pose and style transfer.
result Realistic high-resolution images of models in custom outfits.
Convolutional autoregressive models have recently demonstrated state-of-the-art performance on a number of generation tasks. While fast, parallel training methods have been crucial for their success, generation is typically implemented in a naïve fashion where redundant computations are unnecessarily repeated. This res…
We consider a firm that sells a large number of products to its customers in an online fashion. Each product is described by a high dimensional feature vector, and the market value of a product is assumed to be linear in the values of its features. Parameters of the valuation model are unknown and can change over time.…
Few summaries enable automatic summarization of product reviews.
problem Lack of large labeled datasets for training supervised models in opinion summarization.
method Conditional Transformer model trained to generate summaries given other reviews, fine-tuned to predict summary properties.
result Few summaries (5-10) are sufficient to generate fluent, informative, and sentiment-preserving summaries.
A CNN-SVM model outperforms CNN-Softmax on MNIST and Fashion-MNIST datasets.
problem Improving image classification accuracy using CNN and SVM.
method Combining CNN and SVM in an architecture, using MNIST and Fashion-MNIST datasets.
result CNN-SVM model achieved ~99.04% test accuracy on MNIST, ~90.72% on Fashion-MNIST.
Generates detailed fashion feedback from outfit images.
problem Creating informative and diverse fashion feedback from outfit images.
method Trained deep generative models with visual attention, then improved with Maximum Mutual Information objective function.
result Generated sentences are more diverse and detailed.
Generates outfits for e-commerce using neural networks.
problem Manual outfit creation by stylists is inefficient and not scalable.
method Multilayer neural network with visual and textual features.
result Generated outfits are preferred by users 21-34% more often.
MoVAE learns one-shot classification from unlabeled data.
problem Deep learning requires large labeled data, one-shot learning tackles this issue.
method MoVAE uses a mixture of variational autoencoders to learn from one labeled sample per class.
result MoVAE outperforms state-of-the-art one-shot learning algorithms on multiple datasets.
The paper disentangles multiple input conditions in GANs for fashion design.
problem Controlling multiple input attributes in GANs for realistic image generation.
method Customized conditional GANs with consistency loss functions.
result The method can generate novel and realistic images of clothing articles.
CSPNs combine SPNs and neural networks for tractable probabilistic modeling.
problem Combining tractable SPNs with neural networks' expressiveness.
method Developed CSPNs by conditioning SPN parameters on inputs and learning structure from data.
result CSPNs outperform other models in multilabel image classification.
Algorithm generates original fashion designs for design inspiration.
problem Creating original and compelling fashion designs using AI.
method Investigated different GAN architectures and loss functions for creativity in fashion generation.
result Generated designs are considered original and creative by human subjects.
O-GANs improve fashion image generation by conditioning on a hierarchical ontology.
problem Challenges in training GANs to generate images from text descriptions.
method Ontology Generative Adversarial Networks (O-GANs) that condition on a hierarchical fashion ontology.
result O-GANs achieve better image quality and conditioning results compared to standard GANs.
Dropout controls model capacity in deep learning and matrix completion.
problem Controlling model capacity in deep learning and matrix completion problems.
method Investigates dropout's effect on model capacity and Rademacher complexity.
result Dropout induces a regularizer that controls model capacity in expectation.
We extend the construction of the Hennings TQFT for ribbon Hopf algebras to the case of ribbon quasi-Hopf algebras as defined by Drinfeld. Calculations proceed in a similar fashion to the ordinary Hopf algebra case, but also require the handling of the non-trivial coassociator in the triple tensor product of the algebr…
ART automates synthetic biology design with machine learning.
problem Long development times in synthetic biology due to ad-hoc engineering.
method Machine learning and probabilistic modeling for systematic design.
result ART provides optimized strain recommendations and production levels.
We define quantum exterior product wedge_h and quantum exterior differential d_h on Poisson manifolds (of which symplectic manifolds are an important class of examples). Quantum de Rham cohomology, which is a deformation quantization of de Rham cohomology, is defined as the cohomology of d_h. We also define quantum Dol…
Test for fairness in IR systems based on protected variables.
problem Unfairness in IR systems due to correlation with protected variables.
method Statistical test for 'distribution parity' in top-K IR results.
result Ensures fairness in IR systems for all users.
New model uses PEPS for image classification, outperforming tree-like networks.
problem Efficiently modeling and classifying 2D data like images.
method Feature map followed by PEPS contraction with trainable parameters.
result Significantly superior to tree-like networks on MNIST and Fashion-MNIST.
Generates convincing swapped images of fashion articles on people.
problem Automatic swapping of clothing on fashion model photos.
method Conditional Analogy Generative Adversarial Network (CAGAN) based on adversarial training and deep convolutional neural networks.
result Plausible segmentation masks and convincing swapped images.
The performance of EM in learning mixtures of product distributions often depends on the initialization. This can be problematic in crowdsourcing and other applications, e.g. when a small number of 'experts' are diluted by a large number of noisy, unreliable participants. We develop a new EM algorithm that is driven by…
Deep learning predicts fit for fashion e-commerce.
problem Predicting correct fit for customer satisfaction and cost reduction.
method Deep learning content-collaborative approach using customer and article embeddings.
result Significant improvement over state-of-the-art methods.