Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing

• URL: http://arxiv.org/abs/2402.03379v1
• Date: Sun, 4 Feb 2024 03:30:25 GMT
• Title: Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing
• Authors: Yinqiu Huang, Shuli Wang, Min Gao, Xue Wei, Changhao Li, Chuan Luo, Yinhua Zhu, Xiong Xiao, Yi Luo
• Abstract summary: Uplift modeling, vital in online marketing, seeks to accurately measure the impact of various strategies, such as coupons or discounts, on different users by predicting the Individual Treatment Effect (ITE) This paper introduces the Entire Chain UPlift method with context-enhanced learning (ECUP), devised to tackle these issues. ECUP consists of two primary components: 1) the Entire Chain-Enhanced Network, which utilizes user behavior patterns to estimate ITE throughout the entire chain space, and integrates task prior information to enhance context awareness across all stages, capturing the impact of treatment on different tasks, and 2) the Treatment-Enhanced Network, which facilitates
• Score: 23.18637871568023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Uplift modeling, vital in online marketing, seeks to accurately measure the impact of various strategies, such as coupons or discounts, on different users by predicting the Individual Treatment Effect (ITE). In an e-commerce setting, user behavior follows a defined sequential chain, including impression, click, and conversion. Marketing strategies exert varied uplift effects at each stage within this chain, impacting metrics like click-through and conversion rate. Despite its utility, existing research has neglected to consider the inter-task across all stages impacts within a specific treatment and has insufficiently utilized the treatment information, potentially introducing substantial bias into subsequent marketing decisions. We identify these two issues as the chain-bias problem and the treatment-unadaptive problem. This paper introduces the Entire Chain UPlift method with context-enhanced learning (ECUP), devised to tackle these issues. ECUP consists of two primary components: 1) the Entire Chain-Enhanced Network, which utilizes user behavior patterns to estimate ITE throughout the entire chain space, models the various impacts of treatments on each task, and integrates task prior information to enhance context awareness across all stages, capturing the impact of treatment on different tasks, and 2) the Treatment-Enhanced Network, which facilitates fine-grained treatment modeling through bit-level feature interactions, thereby enabling adaptive feature adjustment. Extensive experiments on public and industrial datasets validate ECUPs effectiveness. Moreover, ECUP has been deployed on the Meituan food delivery platform, serving millions of daily active users, with the related dataset released for future research.

Related papers

• TSCAN: Context-Aware Uplift Modeling via Two-Stage Training for Online Merchant Business Diagnosis [2.8438369256032416]
  We propose a Context-Aware uplift model based on the Two-Stage training approach (TSCAN) In the first stage, we train an uplift model, called CAN-U, which includes the treatment regularizations of IPM and propensity score prediction. In the second stage, we train a model named CAN-D, which utilizes an isotonic output layer to directly model uplift effects.
  arXiv  Detail & Related papers  (2025-04-26T10:00:16Z)
• Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework [81.29965270493238]
  We develop a specialized dataset aimed at enhancing the evaluation and fine-tuning of large language models (LLMs) for wireless communication applications. The dataset includes a diverse set of multi-hop questions, including true/false and multiple-choice types, spanning varying difficulty levels from easy to hard. We introduce a Pointwise V-Information (PVI) based fine-tuning method, providing a detailed theoretical analysis and justification for its use in quantifying the information content of training data.
  arXiv  Detail & Related papers  (2025-01-16T16:19:53Z)
• Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth [12.243349396069934]
  We propose a Multi-Treatment Multi-Task (MTMT) uplift network to estimate treatment effects in a multi-task scenario. MTMT encodes user features and treatments to measure natural responses per task. MTMT has been deployed in our gaming platform to improve user experience.
  arXiv  Detail & Related papers  (2024-08-23T02:44:08Z)
• Learning to Transfer for Evolutionary Multitasking [30.01466615418299]
  Evolutionary multitasking (EMT) is an emerging approach for solving multitask optimization problems (MTOPs) Current approaches in implicit EMT face challenges in adaptability, due to the use of a limited number of evolution operators. We propose a novel Learning to Transfer (L2T) framework to automatically discover efficient KT policies for the MTOPs.
  arXiv  Detail & Related papers  (2024-06-20T14:31:24Z)
• Explicit Feature Interaction-aware Uplift Network for Online Marketing [25.325081105648096]
  uplift modeling aims to accurately capture the degree to which different treatments motivate different users. We propose an explicit feature interaction-aware uplift network (EFIN) to address these two problems. Our EFIN has been deployed in a credit card bill payment scenario of a large online financial platform with a significant improvement.
  arXiv  Detail & Related papers  (2023-06-01T03:26:11Z)
• Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval [152.3504607706575]
  This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks. We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
  arXiv  Detail & Related papers  (2022-06-17T15:40:45Z)
• Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction [74.52904110197004]
  We propose Neighbor-Interaction based CTR prediction, which put this task into a Heterogeneous Information Network (HIN) setting. In order to enhance the representation of the local neighbourhood, we consider four types of topological interaction among the nodes. We conduct comprehensive experiments on two real world datasets and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly.
  arXiv  Detail & Related papers  (2022-01-25T12:44:23Z)
• CMA-CLIP: Cross-Modality Attention CLIP for Image-Text Classification [18.78457628409226]
  We propose the Cross-Modality Attention Contrastive Language-Image Pre-training (CMA-CLIP) CMA-CLIP unifies two types of cross-modality attentions, sequence-wise attention and modality-wise attention, to effectively fuse information from image and text pairs. We conduct experiments on a Major Retail Website Product Attribute (MRWPA) dataset and two public datasets, Food101 and Fashion-Gen.
  arXiv  Detail & Related papers  (2021-12-07T08:23:42Z)
• Distributed Adaptive Learning Under Communication Constraints [54.22472738551687]
  This work examines adaptive distributed learning strategies designed to operate under communication constraints. We consider a network of agents that must solve an online optimization problem from continual observation of streaming data.
  arXiv  Detail & Related papers  (2021-12-03T19:23:48Z)
• Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization [74.34699679568818]
  Weakly supervised temporal action localization (WS-TAL) is a challenging task that aims to localize action instances in the given video with video-level categorical supervision. We propose a cross-modal consensus network (CO2-Net) to tackle this problem.
  arXiv  Detail & Related papers  (2021-07-27T04:21:01Z)
• Deep Active Learning with Augmentation-based Consistency Estimation [23.492616938184092]
  We propose a methodology to improve generalization ability, by applying data augmentation-based techniques to an active learning scenario. For the data augmentation-based regularization loss, we redefined cutout (co) and cutmix (cm) strategies as quantitative metrics. We have shown that the augmentation-based regularizer can lead to improved performance on the training step of active learning.
  arXiv  Detail & Related papers  (2020-11-05T05:22:58Z)
• Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction [166.87111665908333]
  We propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL) Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks.
  arXiv  Detail & Related papers  (2020-04-29T02:32:04Z)
• Cross-modality Person re-identification with Shared-Specific Feature Transfer [112.60513494602337]
  Cross-modality person re-identification (cm-ReID) is a challenging but key technology for intelligent video analysis. We propose a novel cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and the modality-specific characteristics.
  arXiv  Detail & Related papers  (2020-02-28T00:18:45Z)

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.