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.
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48 results for “fashion item prediction”
Model generates outfits and extracts style information from them.
problem Lack of methods focusing on global outfit style information.
method Incorporated unsupervised style extraction module into a bidirectional LSTM and autoencoder model.
result Generated outfits more flexibly and extracted easily interpretable styles.
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.
Outfittery uses machine learning to help stylists choose appropriate fashion items.
problem Selecting appropriate fashion items and ensuring relevance to customers.
method Combining machine learning with human expertise to recommend items by style fit and relevance.
result The method successfully recommends fashion items by style fit and relevance.
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.
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.
ImplicitCE recommends items to new users by co-embedding users and items.
problem Recommendation for new users with no feedback structure.
method End-to-end learning of embedding spaces and transformation function, maximizing correlations between model predictions and user affinities.
result ImplicitCE outperforms state-of-the-art algorithms on Twitter and DBLP datasets.
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.
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.
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.
Framework learns item representations from text data for complementary and similar items.
problem Generating accurate complementary item recommendations from textual data.
method Quadruplet network learning framework for latent space representation of items.
result Items are placed closer together in latent space for similar and complementary items compared to non-complementary items.
Model-based methods for recommender systems have been studied extensively in recent years. In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult. To overcome the calculation barriers, mod…
New deep learning model for matching sets of items, preserving exchangeability.
problem Matching two different sets of items while preserving exchangeability.
method Exchangeable deep neural networks architecture and efficient training framework.
result Significant improvements in fashion set recommendation and group re-identification.
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…
JIMA uses multi-level preference data to recommend composite items.
problem Recommending composite items efficiently with multi-level preference information.
method Joint Interaction Modeling (JIMA) approach that integrates multi-level preference data and interactions.
result JIMA outperforms advanced baselines in offline and online settings.
RecoBERT uses a language model to recommend items from catalogs.
problem Harnessing language models for text-based item recommendations.
method RecoBERT is a BERT-based approach that learns specialized language models for item recommendations without requiring labeled data.
result RecoBERT outperforms other techniques in inferring item similarities from textual catalogs.
This study tackles mutual fund portfolio prediction, focusing on novel items.
problem Predicting novel items in mutual fund portfolios is challenging and less explored.
method Created a comprehensive benchmark dataset and evaluated various recommender system models.
result Autoencoder-based approaches outperform state-of-the-art models in predicting novel items.
Recommendation or Discrimination?: Quantifying Distribution Parity in Information Retrieval Systemsstat.ML
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.
In general, neural networks are not currently capable of learning tasks in a sequential fashion. When a novel, unrelated task is learnt by a neural network, it substantially forgets how to solve previously learnt tasks. One of the original solutions to this problem is pseudo-rehearsal, which involves learning the new t…
New methods improve recommendation accuracy for users and items with few ratings.
problem Skewed distribution and low ratings affect recommendation accuracy.
method Four matrix completion-based approaches: FARP, TMF, TMF + Dropout, IFWMF.
result Improved prediction accuracy for users and items with few ratings.
Predicts next item in sequential bundles using Transformers.
problem Predicting next item in sequentially consumed bundles.
method Used custom Transformers, GPT-3, LSTM, reinforcement learning, Markov models.
result Custom Transformer with decoder-only architecture most accurate.
JODIE learns dynamic user-item embeddings from interactions, outperforming existing methods.
problem Modeling dynamic user-item interactions for accurate future predictions.
method JODIE uses coupled recurrent models with update, projection, and prediction components, and a novel t-Batch algorithm.
result JODIE outperforms state-of-the-art methods by up to 22.4% on future interaction and state change prediction tasks.
A new multi-task framework for recommender systems improves ranking and rating predictions.
problem Improving ranking and rating predictions in recommender systems.
method Exploits a two-phase decision process: first deciding to interact with an item (ranking task) and then rating it (rating prediction task).
result Superior performance compared to state-of-the-art methods on two benchmark datasets.
New method uses bandit feedback to better evaluate recommender systems.
problem Traditional offline evaluation of recommender systems is inaccurate.
method Exploits bandit feedback to estimate online performance.
result Bandit feedback provides more accurate offline evaluation.
Study integrates diverse data sources to predict mental health conditions.
problem Predict individuals' mental health conditions using a heterogeneous network approach.
method Leverage a heterogeneous information network (HIN) to model social interaction, health data, and survey data. Apply recommender system (RS) and node classification (NC) paradigms to predict mental health states.
result RS and NC methods outperform traditional logistic regression models in predicting mental health conditions.
New neural network predicts user-item relationships in evolving graphs.
problem Link prediction in dynamic graphs for recommendation services.
method Proposes a new neural network approach to leverage temporal contextual information.
result Our approach produces better predictions in scenarios with changing user-item relationships.
Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant? To help answer this question, we design and investigate different image generation models associated with different loss functions to boost creativity in fashion generation. The dimensions of our explorations include…
New algorithms for set union in differential privacy improve efficiency and accuracy.
problem Efficiently discovering items from private user data in natural language processing.
method Developed algorithms that allow users to contribute items in a dependent fashion, guided by a policy with contractive properties.
result New algorithms significantly outperform existing mechanisms in terms of efficiency and accuracy.
Item neighbourhood methods for collaborative filtering learn a weighted graph over the set of items, where each item is connected to those it is most similar to. The prediction of a user's rating on an item is then given by that rating of neighbouring items, weighted by their similarity. This paper presents a new neigh…
Seq2Slate models for ranking by predicting appealing item slates.
problem Ranking items in a slate to present a user appealing set of items.
method Sequence-to-sequence model that predicts the next best item to place on the slate.
result The model captures complex dependencies between items and learns from weak supervision.
The paper improves recommendation models by considering user interactions with recommended items.
problem Improving next item prediction in recommendation systems.
method Extending RNN framework with a recommendation action module and state-action fusion module.
result Improved performance on next item prediction compared to baselines.
The paper explores using set-level ratings for better user-item preference prediction in recommender systems.
problem Capturing user preferences on individual items using set-level ratings.
method Developed collaborative filtering-based methods to model user behaviors in set-level ratings.
result Collaborative filtering-based models can recover and predict user preferences on individual items using set-level ratings.
Network-based models predict user preferences for items like movies and research articles.
problem Filtering and delivering personalized advice for users with many available products.
method Network models based on group memberships, using Monte Carlo sampling and Expectation-Maximization methods.
result Network models outperform leading approaches for recommendation.
KGAT uses knowledge graphs to improve recommendation accuracy and explainability.
problem Accurate, diverse, and explainable recommendations require side information and collaborative signals.
method KGAT models high-order relations in a knowledge graph by propagating embeddings and using attention mechanisms.
result KGAT significantly outperforms state-of-the-art methods on public benchmarks.
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrievalcs.LG
Improves search performance by transferring knowledge from recommender system.
problem Cold start and feedback loop problems in search retrieval.
method Zero-Shot Heterogeneous Transfer Learning framework.
result Significant improvements in relevance and user interactions over production system.
WCF uses Wasserstein distance to recommend cold-start items based on content similarity.
problem Recommendation performance drops for new items with little interaction history.
method Applies Wasserstein distance to map interaction history to contents, inferring user preferences.
result WCF outperforms state-of-the-art methods in cold-start recommendation.
Improved item recommendations for repeat interactions using sequence analysis.
problem Limited effectiveness of traditional recommender systems in handling repeated user-item interactions.
method Designed a recommender system that considers sequences of item interactions for each user.
result Empirically shown to give highly accurate predictions and increase sales by 5%.
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.
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.
In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new ite…
OMIC improves matrix completion with orthonormal side information and nuclear-norm regularization.
problem Matrix completion with improved interpretability and adaptability.
method OMIC combines orthonormal side information and nuclear-norm regularization, optimized by a converging algorithm.
result OMIC outperforms state-of-the-art methods in synthetic and real-world datasets.
The paper models and predicts co-occurrence counts using Gamma regression.
problem Predicting relevance between items or users from high-dimensional sparse co-occurrence count data.
method Shared parameter alternating zero-inflated Gamma regression models (SA-ZIG) with Fisher scoring and learning rate adjustment.
result SA-ZIG with learning rate adjustment performs satisfactorily in predicting relevance.
KGCN uses KGs to improve recommender systems by connecting user and item attributes.
problem Collaborative filtering sparsity and cold start issues.
method End-to-end framework that captures inter-item relatedness using KGs and minibatch sampling.
result KGCN outperforms strong recommender baselines on movie, book, and music recommendation datasets.
Explicit Feedbacks Meet with Implicit Feedbacks : A Combined Approach for Recommendation Systemcs.IR
Paper proposes a combined model for better recommendation by integrating explicit and implicit feedbacks.
problem Improve recommendation accuracy by considering both explicit and implicit feedbacks.
method Developed three models (RHC-PMF, RV-PMF, RHCV-PMF) that incorporate users' explicit and implicit feedbacks for better rating prediction.
result RHCV-PMF model outperforms other models in cold start scenarios for both users and items.
SR-GNN models sessions as graphs to predict user actions.
problem Predict user actions based on anonymous sessions.
method SR-GNN models sessions as graphs and uses attention networks.
result SR-GNN outperforms state-of-the-art methods consistently.
Models for recommender systems show similar results in item availability.
problem Model uncertainty in recommender systems.
method Examined different variations of a model for item availability using predictive multiplicity.
result Most models produce similar results in terms of item availability discrepancy.
New cold-start recommendation method for recommender systems.
problem Making accurate recommendations for users and items with limited data.
method Proposes a faster cold-start prediction formula in collective matrix factorization models.
result Improved cold-start recommendations with reliable predictions for new users, better than non-personalized recommendations.
Next basket recommendation improved with context-aware item representations.
problem Predicting users' next purchases based on historical transactions.
method Pre-trained context-aware item representations using transformers.
result IERT outperforms state-of-the-art methods in next basket prediction.