ADT4Coupons: An Innovative Framework for Sequential Coupon Distribution in E-commerce
- URL: http://arxiv.org/abs/2508.09198v1
- Date: Fri, 08 Aug 2025 13:03:17 GMT
- Title: ADT4Coupons: An Innovative Framework for Sequential Coupon Distribution in E-commerce
- Authors: Li Kong, Bingzhe Wang, Zhou Chen, Suhan Hu, Yuchao Ma, Qi Qi, Suoyuan Song, Bicheng Jin,
- Abstract summary: Coupon distribution is a critical marketing strategy used by online platforms to boost revenue and enhance user engagement.<n>Existing coupon distribution strategies fall far short of effectively leveraging the complex sequential interactions between platforms and users.
- Score: 4.671198197397224
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Coupon distribution is a critical marketing strategy used by online platforms to boost revenue and enhance user engagement. Regrettably, existing coupon distribution strategies fall far short of effectively leveraging the complex sequential interactions between platforms and users. This critical oversight, despite the abundance of e-commerce log data, has precipitated a performance plateau. In this paper, we focus on the scene that the platforms make sequential coupon distribution decision multiple times for various users, with each user interacting with the platform repeatedly. Based on this marketing scenario, we propose a novel marketing framework, named Aligned Decision Transformer for Coupons (ADT4Coupons), to directly devise coupon distribution policy for long-term revenue boosting. ADT4Coupons enables optimized online decision-making in a variety of real-world marketing scenarios. It achieves this by seamlessly integrating three key characteristics, general scenarios, sequential modeling with more comprehensive historical data, and efficient iterative updates within a unified framework. Furthermore, empirical results on real-world industrial dataset, alongside public and synthetic datasets demonstrate the superiority of our framework.
Related papers
- OneMall: One Architecture, More Scenarios -- End-to-End Generative Recommender Family at Kuaishou E-Commerce [68.7552227901176]
OneMall is an end-to-end generative recommendation framework tailored for e-commerce services at Kuaishou.<n>It unifies the e-commerce's multiple item distribution scenarios, such as Product-card, short-video and live-streaming.<n>OneMall has been deployed, serving over 400 million daily active users at Kuaishou.
arXiv Detail & Related papers (2026-01-29T14:22:39Z) - Distribution-Controlled Client Selection to Improve Federated Learning Strategies [4.254099382808598]
Federated learning (FL) is a distributed learning paradigm that allows multiple clients to jointly train a shared model.<n>The presence of data imbalance among clients is a thread to the success of FL, as it causes the performance of the shared model to decrease.<n>We propose an extension to existing FL strategies, which selects active clients that best align the current label distribution with one of two target distributions.
arXiv Detail & Related papers (2025-09-25T08:07:13Z) - Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertising [30.108437268612438]
Bidding-Aware Retrieval (BAR) is a model-based retrieval framework that addresses multi-stage inconsistency by incorporating ad bid value into the retrieval scoring function.<n>BAR's core innovation is Bidding-Aware Modeling, incorporating bid signals through monotonicity-constrained learning and multi-task distillation to ensure economically coherent representations.<n>Extensive offline experiments and full-scale deployment across Alibaba's display advertising platform validated BAR's efficacy.
arXiv Detail & Related papers (2025-08-07T09:43:34Z) - Session-Level Dynamic Ad Load Optimization using Offline Robust Reinforcement Learning [14.410333601657172]
Session-level dynamic ad load optimization aims to personalize the density and types of delivered advertisements in real time during a user's online session.<n>Traditional causal learning-based approaches struggle with key technical challenges.<n>We develop an offline deep Q-network (DQN)-based framework that effectively mitigates confounding bias in dynamic systems.
arXiv Detail & Related papers (2025-01-09T21:53:03Z) - Robust portfolio optimization model for electronic coupon allocation [6.729713185413412]
We apply a robust portfolio optimization model based on customer segmentation to the coupon allocation problem.
Our results open up great potential for robust portfolio optimization as an effective tool for practical coupon allocation.
arXiv Detail & Related papers (2024-05-21T15:30:25Z) - A Bargaining-based Approach for Feature Trading in Vertical Federated
Learning [54.51890573369637]
We propose a bargaining-based feature trading approach in Vertical Federated Learning (VFL) to encourage economically efficient transactions.
Our model incorporates performance gain-based pricing, taking into account the revenue-based optimization objectives of both parties.
arXiv Detail & Related papers (2024-02-23T10:21:07Z) - An Auction-based Marketplace for Model Trading in Federated Learning [54.79736037670377]
Federated learning (FL) is increasingly recognized for its efficacy in training models using locally distributed data.
We frame FL as a marketplace of models, where clients act as both buyers and sellers.
We propose an auction-based solution to ensure proper pricing based on performance gain.
arXiv Detail & Related papers (2024-02-02T07:25:53Z) - VFed-SSD: Towards Practical Vertical Federated Advertising [53.08038962443853]
We propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations.
Specifically, we develop a self-supervised task MatchedPair Detection (MPD) to exploit the vertically partitioned unlabeled data.
Our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.
arXiv Detail & Related papers (2022-05-31T17:45:30Z) - Approaching sales forecasting using recurrent neural networks and
transformers [57.43518732385863]
We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques.
Our empirical results show how good performance can be achieved by using a simple sequence to sequence architecture with minimal data preprocessing effort.
The proposed solution achieves a RMSLE of around 0.54, which is competitive with other more specific solutions to the problem proposed in the Kaggle competition.
arXiv Detail & Related papers (2022-04-16T12:03:52Z) - Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential
Advertising [52.3825928886714]
We formulate the sequential advertising strategy optimization as a dynamic knapsack problem.
We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space.
To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach.
arXiv Detail & Related papers (2020-06-29T18:50:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.