arXiv Machine Learning

Predictive Analytics in E-Commerce for CustomerBehavior Forecasting using hybrid Ret-DNN withXGBoost Model

arXiv:2606. 17931v1 Announce Type: new Abstract: In recent years, electronic (E) commerce services have rapidly increased in the daily lives of people, which helpsthem to purchase products online.

Hugging Face Trending Papers
Jun 8

FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting

Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectral patterns, and contextual sensitivity differ substantially. A single forecasting model rarely performs well across all regimes, while dense ensembles increase inference cost and provide limited insight into expert suitability.

arXiv AI
Jul 9

Large Behavior Model: A Promptable Digital Twin of the Retail Customer

arXiv:2607. 06993v1 Announce Type: new Abstract: Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy without explaining decisions or simulate users without grounding them in real behavioral data.

By Wachiravit Modecrua, Krittin Pachtrachai, Touchapon Kraisingkorn
Hugging Face Trending Papers
Aug 12

Dual-Model Sentiment Analysis of Consumer Reviews in the Retail Coffee Sector Using Machine Learning and Deep Learning Approaches

Consumer reviews play an important role in shaping brand perception and business strategies, particularly in service-driven industries such as retail coffee. This study presents a comparative sentiment analysis framework for Starbucks customer reviews using classical machine learning and deep learning approaches.

arXiv AI
Jun 2

ChurnNet: A Optimized Modern AI for Churn Prediction

arXiv:2606. 00169v1 Announce Type: cross Abstract: Increased competition and the growing similarity of products and services offered by retailers have lowered the barriers for customers to switch to competitors.

By Syed Saad Saif, Giulio Maggiore, Paolo Russo, Damiano Distante
arXiv Machine Learning
Jun 15

High-Frequency Pricing at Scale for E-Commerce

arXiv:2606. 13741v1 Announce Type: new Abstract: This paper presents the design, development, and implementation of a specialized forecast-then-optimize algorithmic pricing tool for sales campaigns in fashion e-commerce.

By Stefan Birr, Tobias Huelden, Mones Raslan, Adele Gouttes, Andreas Schmitt, Mateusz Koren, Johannes Stephan, Robert Streek, Manuel Kunz, Tim Januschowski