Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness

DOI10.1287/ijoc.2021.1147OpenAlexW4225838779WikidataQ114058185 ScholiaQ114058185MaRDI QIDQ5087729

Gediminas Adomavicius, William Li, Annie Qu, Xuan Bi

Publication date: 1 July 2022

Published in: INFORMS Journal on Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2011.03452

zbMATH Keywords

machine learningtensor decompositionconsumer demandsales forecastingdesign science

Mathematics Subject Classification ID

Operations research, mathematical programming (90-XX)

Cites Work

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• Estimating Vector Autoregressions with Panel Data
• Demand forecasting with high dimensional data: the case of SKU retail sales forecasting with intra- and inter-category promotional information
• Customer reviews for demand distribution and sales nowcasting: a big data approach
• Multilayer tensor factorization with applications to recommender systems
• Pathwise coordinate optimization
• Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of ``Eckart-Young decomposition
• Optimization-Based Approaches for Maximizing Aggregate Recommendation Diversity
• Do Inventory and Gross Margin Data Improve Sales Forecasts for U.S. Public Retailers?
• A Logistic Factorization Model for Recommender Systems With Multinomial Responses
• Incorporating Aggregate Diversity in Recommender Systems Using Scalable Optimization Approaches
• Recommender Systems Handbook
• Learning representations by back-propagating errors
• Statistical significance of the Netflix challenge