Multi-channel sellers traffic allocation in large-scale e-commerce promotion

Abstract: Large-scale online promotions, such as Double 11 and Black Friday, are of great value to e-commerce platforms nowadays. Traditional methods are not successful when we aim to maximize global Gross Merchandise Volume (GMV) in the promotion scenarios due to three limitations. The first is that the GMV of sellers varies significantly from daily scenarios to promotions. Second, these methods do not consider explosive demands in promotions, so that a consumer may fail to purchase some popular items due to sellers’ limited capacities. Third, the traffic distribution over sellers presents divergence in different channels, thus rendering the performance of the traditional single-channel methods far from optimal in creating commercial values. To address these problems, we design a Multi-Channel Sellers Traffic Allocation (MCSTA) optimization model to obtain optimal page view (PV) distribution concerning global GMV. Then we propose a general constrained non-smooth convex optimization solution with a Multi-Objective Shortest Distance (MOSD) hyperparameter tuning method to solve MCSTA. This is the first work to systematically address this issue in the scenario of large-scale online promotions. The empirical results show that MCSTA achieves significant improvement of GMV by 1.1

02/02/2021

Hu Liu, Jing LU, Xiwei Zhao and
Sulong Xu, Hao Peng, Yutong Liu, Zehua Zhang, Jian Li, Junsheng Jin, Yongjun Bao, Weipeng Yan

Xu HE, Bo An, Yanghua Li and
Haikai Chen, Qingyu Guo, Xin Li, Zhirong Wang

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

5:13

25/07/2020

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

session-based recommendations, comparison of offline evaluation metrics with labels from human experts, evaluation using human experts, A/B test

2:42

06/12/2021

A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems

Yi Ma, Xiaotian Hao, Jianye Hao and
Jiawen Lu, Xing Liu, Tong Xialiang, Mingxuan Yuan, Zhigang Li, Jie Tang, Zhaopeng Meng

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

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

11:21

14/07/2020

Shengyu Zhang, Ziqi Tan, Zhou Zhao and
Jin Yu, Kun Kuang, Tan Jiang, Jingren Zhou, Hongxia Yang, Fei Wu

Zhaoqing Peng, Junqi Jin, Lan Luo and
Yaodong Yang, Rui Luo, Jun Wang, Weinan Zhang, Haiyang Xu, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu, Kun Gai

Computer Vision, 2D and 3D Computer Vision, Structural and Model-Based Approaches, Knowledge Representation and Reasoning, Transportation

11:36