Price investment using prescriptive analytics and optimization in retail

Abstract: As the world’s largest retailer, Walmart’s core mission is to save people money so they can live better. We call the strategy we use to accomplish this goal our Every Day Low Price strategy. By keeping operational expenses as low as possible, we can continually apply a downward pressure on our prices, in turn increasing the amount of traffic, and ultimately, sales within our stores. In this paper, we apply Machine Learning (ML) algorithms and Operations Research techniques for forecasting and optimization to build a new price recommendation system, which improves our ability to generate price recommendations accurately and automatically. Comprised of a demand forecasting step, two optimizations, and causal inference analysis, our system was evaluated in the form of forecast backtests and live pricing experiments, both of which suggested that our approach was more effective than the current rule-based pricing system.

12/07/2020

Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising

Xiaotian Hao, Zhaoqing Peng, Yi Ma and
Guan Wang, Junqi Jin, Jianye Hao, Shan Chen, Rongquan Bai, Mingzhou Xie, Miao Xu, Zhenzhe Zheng, Chuan Yu, HAN LI, Jian Xu, Kun Gai

package type selection, ordinal enforcement, discrete optimization, constrained-unconstrained formulation equivalence, trade off parameter selection

19:13

02/02/2021

BCORLE($\lambda$): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market

Price forecast with high-frequency finance data: An autoregressive recurrent neural network model with technical indicators

Yuechun Gu, Da Yan, Sibo Yan, Zhe Jiang

Reinforcement Learning and Planning, Reinforcement Learning, Reinforcement Learning and Planning, Markov Decision Processes, Algorithms, Components Analysis (e.g., CCA, ICA, LDA, PCA)

5:13

14/09/2020

information retrieval, ranking and preference learning, learning to rank, e-commerce search, implicit feedback, counterfactual risk minimization, dataset, mining data logs

11:21

25/07/2020

Liyi Guo, Rui Lu, Haoqi Zhang and
Junqi Jin, Zhenzhe Zheng, Fan Wu, Jin Li, Haiyang Xu, Han Li, Wenkai Lu, Jian Xu, Kun Gai

catastrophic forgetting, representation learning, recommendation, incremental learning, continual learning, recommender, graph neural networks, online learning, recommendation system

9:28

14/06/2020

cross-modal analysis, food retrieval, image-to-recipe, latent variable, cross-modal attention, metric learning, representation learning, variational auto-encoder, natural language processing

1:01

22/09/2020