Study compares three performance metrics of Bangladeshi banks.
problem Comparing different performance metrics of commercial banks.
method Empirical framework using MPI, ROA, TSR over 2011-2015.
result Productivity growth was recorded, but profitability and stock performance were negative.
Unified product embeddings improve cross-task performance in e-commerce.
problem Training product embeddings in isolation limits cross-task performance.
method Combining text, clickstream, and image data using denoising auto-encoders, BPR, and Siamese neural networks.
result Unified product embeddings uniformly outperform isolated embeddings across three e-commerce tasks.
Paper proposes efficient AL algorithms for optimizing product performance under environmental variability.
problem Optimizing product performance under varying environmental conditions.
method Formulated as Bayesian Quadrature Optimization problems for probabilistic threshold robustness measure using Gaussian Process model.
result Proposed algorithms provide credible intervals for probabilistic threshold robustness measure and demonstrate efficiency in real-world applications.
The study finds cash productivity predicts stock performance in a specific subset of firms.
problem Predicting future stock performance using cash productivity.
method Using financial and market data, calculated cash returns as a proxy for operational efficiency, and tested a long-only strategy on Nasdaq-listed non-financial firms.
result Cash productivity has significant predictive power in a handpicked portfolio but limited across the broader Nasdaq universe.
The paper finds that faster technological improvement leads to faster diffusion of products.
problem The relationship between technological improvement and innovation diffusion is not well understood.
method Empirical tests across multiple products and technologies.
result Faster diffusion for products based on more rapidly improving technological domains.
The paper tackles imbalance in production data by proposing sampling methods to improve model performance on underrepresented observations.
problem Imbalance in production data negatively impacts model predictive performance on underrepresented observations.
method Three sampling approaches are investigated to adjust for imbalance in training data and improve model performance.
result Fitting a model using sampled data yields a small reduction in overall predictive performance but a better performance on underrepresented observations.
Framework for monitoring ML model performance in production without labels.
problem Monitoring real-time prediction quality of ML models in production without labels.
method ML Health framework using diagnostic methods to generate alerts for further investigation.
result The method outperforms standard distance metrics at detecting issues with mismatched data sets.
US firms improve ESG performance in response to China trade shock.
problem Impact of China trade shock on US ESG performance.
method Trade policy experiment exploiting tariff changes.
result Greater import competition from China increases US firm ESG performance.
Model learns product vectors from baskets and browsing sessions for better complementary product recommendations.
problem Inferring complementary products from basket and browsing data.
method Proposes BB2vec model that learns product vectors from both baskets and browsing sessions.
result The BB2vec model improves complementary product recommendations and alleviates the cold start problem.
ABIPNN improves neural network performance by processing vectors in each neuron.
problem Traditional neural networks fail to model associations among adjacent scalars.
method ABIPNN uses arbitrary bilinear products to process vector-valued neurons.
result ABIPNN outperforms conventional neural networks in multispectral image denoising and singing voice separation.
A new method improves maximum inner product search by locally decomposing residual vectors.
problem Maximum inner product search efficiency and accuracy.
method Local Orthogonal Decomposition (LOD) combined with multiscale quantization.
result LOD consistently achieves higher recall than previous methods under the same bitrates.
Proposes using entity embedding vectors to improve Gaussian Process models for knowledge transfer across cell lines.
problem Lack of reuse of experimental data for predicting novel processes.
method Hybrid Gaussian Process models with entity embedding vectors to represent product identity.
result Improved performance in predicting novel processes compared to traditional methods.
New risk theory for 'Pay-for-Performance' models.
problem How to price and hedge operational and financial risks in new business models.
method Developed a new risk theory and calculation method for 'Pay-for-Performance' models.
result Presented a model for determining risk premiums including both financial and operational risks.
Uniswap markets perform well and closely track reference prices.
problem Understanding and validating the performance of Uniswap markets.
method Formal analysis and numerical simulation of constant product markets.
result Uniswap markets closely track reference prices under common conditions.
OMBA learns product and user representations for better online market basket analysis.
problem Limited ability to uncover rarely occurring and temporal associations in MBA.
method Jointly learns product and user representations, captures temporal dynamics, scalable online method.
result OMBA outperforms state-of-the-art methods by 21% on real-world datasets.
Dynamic pricing improves sales of low-sale products using online clustering.
problem Low-sale products lack sufficient data for traditional dynamic pricing algorithms.
method Online clustering of product demand and dynamic pricing decisions based on cluster analysis.
result The proposed algorithms significantly outperform traditional single-product pricing policies in increasing revenue.
Coherent diversification of tech fields correlates with higher labor productivity.
problem Understanding how firms' technological diversification impacts productivity.
method Analyzed patent data of 70k firms over 2004-2013, defined coherent diversification as network of related tech fields.
result Firms with coherent diversification structure outperform those with scattered diversification in labor productivity.
New Bayesian method for sparse signal recovery using normal product priors.
problem Sparse signal recovery in compressive sensing.
method Developed a two-stage normal product-based hierarchical model using variational Bayesian inference.
result Demonstrated effectiveness through simulations compared to state-of-the-art algorithms.
LineFlow is a framework for training RL agents to control production lines.
problem Designing control systems for production lines is challenging.
method Introduces LineFlow, an extensible Python framework for simulating and training RL agents.
result RL agents approach optimal performance in well-understood scenarios but face challenges in complex industrial lines.
Study reveals structural constraints on income inequality in Latin America.
problem Income inequality in Latin America compared to other economies.
method Product space, Product Gini Index, Xgini coefficient.
result LAC economies are more dependent on products related to high income inequality.
We derive a closed formula for a star-product on complex projective space and on the domain SU(n+1)/S(U(1)×U(n)) using a completely elementary construction: Starting from the standard star-product of Wick type on Cn+1∖{0} and performing a quantum analogue of Marsden-Weinstein reduction, we ca…
This work characterizes topological descriptors of graph products and their expressive power.
problem Capturing multiscale structural information in graph products using topological descriptors.
method Analysis of various filtrations on graph products, including Euler characteristic and persistent homology.
result Persistent homology of graph products contains more information than individual graphs.
Combines gating and tensor products for RNNs to improve performance.
problem Improving RNNs' ability to capture long-term dependencies.
method Proposes a novel RNN architecture combining gating mechanism and tensor products.
result Significant performance improvement on word-level and character-level language modeling tasks.
We propose a robust elastic net (REN) model for high-dimensional sparse regression and give its performance guarantees (both the statistical error bound and the optimization bound). A simple idea of trimming the inner product is applied to the elastic net model. Specifically, we robustify the covariance matrix by trimm…
Improved collaborative filtering with neural network models of reviews.
problem Improve collaborative filtering performance using side information from reviews.
method Introduced two neural network models (product-of-experts and recurrent neural network) to incorporate reviews into collaborative filtering.
result The product-of-experts model achieved state-of-the-art performance, outperforming LDA-based approach.
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.
Improved sales forecasting for new products using transfer learning.
problem Insufficient training data for new products leads to inaccurate sales forecasts.
method Network-based Transfer Learning approach for deep neural networks.
result Deep neural networks' prediction accuracy for food sales forecasting can be effectively increased.
New method uses product embeddings to predict bundle success.
problem Designing effective product bundles in large retail settings.
method Leverage historical purchases and clickstream data to generate product embeddings, then use heuristics for complementarity and substitutability.
result Embeddings-based heuristics predict bundle success, robust across categories and retailers.
Gradient boosting predicts promotion efficiency using multiple performance indicators.
problem Forecasting promotion efficiency in FMCG retail.
method Gradient boosting applied to six performance indicators for different product groups.
result Models accurately forecast promotion efficiency, optimizing marketing strategies.
Observations depending on sums of random variables are common throughout many fields; however, no efficient solution is currently known for performing max-product inference on these sums of general discrete distributions (max-product inference can be used to obtain maximum a posteriori estimates). The limiting step to …
Study optimizes dynamic product selection and pricing using censored preference feedback.
problem Maximizing revenue from dynamic assortment and pricing decisions.
method Proposes a censored multinomial logit model and LCB pricing strategy combined with UCB or TS product selection.
result Achieves optimal regret bounds for dynamic pricing and selection.
Auto-detection system identifies safety issues in baby products from reviews.
problem Early detection of safety issues in baby products to reduce injuries and deaths.
method Text cleaning, feature extraction, dimensionality reduction, classifier analysis.
result Logistic regression model with 66% precision in identifying top 50 safety issues.
Deep learning model improves personalized product recommendations.
problem Large-scale personalized recommendation systems with implicit feedback.
method Combines neural attention mechanisms and representation learning for efficient, high-coverage models.
result Significant performance improvements over alternative methods in offline and online settings.
New datasets support supervised learning for fungal BGC discovery.
problem Lack of labeled data for fungal BGCs.
method Developed new publicly available datasets for supervised learning.
result Supervised learning outperforms data-driven methods in fungal BGC prediction.
WIPS optimizes inner product weights to approximate various similarities.
problem Learning high-quality node representations and accurate similarities.
method Weighted inner product similarity (WIPS) with adjustable weights.
result WIPS can approximate arbitrary general similarities including positive definite and indefinite kernels.
A new matrix Hilbert space framework preserves matrix structure and captures multi-way correlations.
problem Preserving matrix structure and capturing multi-way correlations in matrix learning.
method Introducing matrix Hilbert space and reproducing kernel matrix Hilbert space (RKMHS) to perform matrix inner product space.
result Preserves matrix structure and captures multi-way correlations without tensor decomposition.
Linear classifiers in product space forms improve scRNA-seq data classification.
problem Linear classification in products of Euclidean, spherical, and hyperbolic spaces.
method Novel formulations of linear classifiers on Riemannian manifolds, proving expressive power, and formalizing perceptron and SVM classifiers.
result Linear classifiers in product space forms have the same expressive power as in Euclidean space of the same dimension.
Deep learning model forecasts PV power production with high accuracy.
problem Accurate forecasting of photovoltaic power production is needed for electricity infrastructure.
method Sequence to sequence model with attention mechanism using weather predictions and historical data.
result The model outperforms existing methods in forecast skill score.
Study compares BERT and XLNet for multi-class categorization of product descriptions.
problem Robustness of multi-class categorization using pre-trained contextualized language models.
method Fine-tuning BERT and XLNet on Amazon product data for multi-class classification.
result Performance decreases linearly with the number of class labels, with BERT consistently outperforming XLNet.
A parallel algorithm learns efficient Kronecker product dictionaries.
problem Sparse representation of 2D signals like images and hyperspectral data.
method Highly parallelizable algorithm for learning separable dictionaries.
result Competitive sparse representations at lower computational cost.
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.
Focuses on monitoring and explaining models in real-world applications.
problem Ensuring high quality machine learning services in production environments.
method Statistical techniques for model performance and data monitoring, explanations of predictions.
result Challenges and solutions for implementing monitoring and explanation in production models.
MPP predicts model performance in real-time ML deployments.
problem Lack of labels in production prevents tracking model performance.
method Ensemble of metrics to create a score for prediction quality.
result Automates monitoring and enables scale ML deployments.
The paper improves consumer preference modeling by considering multiple product categories.
problem Estimating consumer preferences across multiple product categories with varying attributes and price sensitivity.
method Extends matrix factorization techniques to account for time-varying product attributes and out-of-stock products, pooling information across categories to estimate heterogeneity in preferences.
result The model improves over traditional approaches, accurately estimating consumer preferences and price sensitivity.
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.
New algorithm reduces cold-start costs in multi-armed bandits for many products.
problem High burn-in costs in multi-armed bandits for new products.
method Two-phase bandit algorithm using subsampling and low-rank matrix estimation.
result Reduces burn-in costs and expedites experiment in large product sets.
A new neural network model ONCF improves collaborative filtering by explicitly modeling embedding correlations.
problem Improving collaborative filtering for better recommendation quality.
method ONCF uses an outer product to model embedding correlations and a convolutional layer to learn high-order correlations.
result ONCF outperforms existing models in implicit feedback data experiments.
Study improves product categorization on Amazon using multi-modal fusion.
problem Multi-label product categorization in e-commerce.
method Late fusion of image, description, and title modalities using modified CNN and ResNet-50 models.
result Tri-modal late fusion model achieved an F1 score of 88.2%, significantly better than single modal models.