Explicit Feature Interaction-aware Uplift Network for Online Marketing

• URL: http://arxiv.org/abs/2306.00315v1
• Date: Thu, 1 Jun 2023 03:26:11 GMT
• Title: Explicit Feature Interaction-aware Uplift Network for Online Marketing
• Authors: Dugang Liu, Xing Tang, Han Gao, Fuyuan Lyu, Xiuqiang He
• Abstract summary: 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.
• Score: 25.325081105648096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a key component in online marketing, uplift modeling aims to accurately capture the degree to which different treatments motivate different users, such as coupons or discounts, also known as the estimation of individual treatment effect (ITE). In an actual business scenario, the options for treatment may be numerous and complex, and there may be correlations between different treatments. In addition, each marketing instance may also have rich user and contextual features. However, existing methods still fall short in both fully exploiting treatment information and mining features that are sensitive to a particular treatment. In this paper, we propose an explicit feature interaction-aware uplift network (EFIN) to address these two problems. Our EFIN includes four customized modules: 1) a feature encoding module encodes not only the user and contextual features, but also the treatment features; 2) a self-interaction module aims to accurately model the user's natural response with all but the treatment features; 3) a treatment-aware interaction module accurately models the degree to which a particular treatment motivates a user through interactions between the treatment features and other features, i.e., ITE; and 4) an intervention constraint module is used to balance the ITE distribution of users between the control and treatment groups so that the model would still achieve a accurate uplift ranking on data collected from a non-random intervention marketing scenario. We conduct extensive experiments on two public datasets and one product dataset to verify the effectiveness of our EFIN. In addition, our EFIN has been deployed in a credit card bill payment scenario of a large online financial platform with a significant improvement.

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)
• Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing [6.511772664252086]
  Uplift modeling is proposed to solve the problem, which applies different treatments (e.g., discounts, bonus) to satisfy corresponding users. In real-world scenarios, there are rich contexts available in the online platform (e.g., short videos, news) and the uplift model needs to infer an incentive for each user. We propose a novel model-agnostic Robust Uplift Modeling with Large-Scale Contexts (UMLC) framework for Real-time Marketing.
  arXiv  Detail & Related papers  (2025-01-04T08:55:50Z)
• STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading [55.02735046724146]
  In financial trading, factor models are widely used to price assets and capture excess returns from mispricing. We propose a Spatio-Temporal factOR Model based on dual vector quantized variational autoencoders, named STORM. Storm extracts features of stocks from temporal and spatial perspectives, then fuses and aligns these features at the fine-grained and semantic level, and represents the factors as multi-dimensional embeddings.
  arXiv  Detail & Related papers  (2024-12-12T17:15:49Z)
• Latent Feature Mining for Predictive Model Enhancement with Large Language Models [2.6334346517416876]
  We introduce an effective approach to formulate latent feature mining as text-to-text propositional logical reasoning. We propose FLAME, a framework that leverages large language models (LLMs) to augment observed features with latent features. We validate our framework with two case studies: the criminal justice system and the healthcare domain.
  arXiv  Detail & Related papers  (2024-10-06T03:51:32Z)
• 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)
• End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling [12.160403526724476]
  We propose a novel End-to-End Cost-Effective Incentive Recommendation (E3IR) model under budget constraints. Specifically, our methods consist of two modules, i.e., the uplift prediction module and the differentiable allocation module. Our E3IR improves allocation performance compared to existing two-stage approaches.
  arXiv  Detail & Related papers  (2024-08-21T13:48:00Z)
• Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing [23.18637871568023]
  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
  arXiv  Detail & Related papers  (2024-02-04T03:30:25Z)
• Multimodal Fusion Interactions: A Study of Human and Automatic Quantification [116.55145773123132]
  We study how humans annotate two categorizations of multimodal interactions. We propose a method to automatically convert annotations of partial and counterfactual labels to information decomposition.
  arXiv  Detail & Related papers  (2023-06-07T03:44:50Z)
• Client-specific Property Inference against Secure Aggregation in Federated Learning [52.8564467292226]
  Federated learning has become a widely used paradigm for collaboratively training a common model among different participants. Many attacks have shown that it is still possible to infer sensitive information such as membership, property, or outright reconstruction of participant data. We show that simple linear models can effectively capture client-specific properties only from the aggregated model updates.
  arXiv  Detail & Related papers  (2023-03-07T14:11:01Z)
• Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation [52.89816309759537]
  Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios. The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input. We propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning framework to learn shared and behavior-specific interests for different behaviors.
  arXiv  Detail & Related papers  (2022-08-03T05:28:14Z)
• Toward Understanding the Influence of Individual Clients in Federated Learning [52.07734799278535]
  Federated learning allows clients to jointly train a global model without sending their private data to a central server. We defined a new notion called em-Influence, quantify this influence over parameters, and proposed an effective efficient model to estimate this metric.
  arXiv  Detail & Related papers  (2020-12-20T14:34:36Z)
• Interactive Fusion of Multi-level Features for Compositional Activity Recognition [100.75045558068874]
  We present a novel framework that accomplishes this goal by interactive fusion. We implement the framework in three steps, namely, positional-to-appearance feature extraction, semantic feature interaction, and semantic-to-positional prediction. We evaluate our approach on two action recognition datasets, Something-Something and Charades.
  arXiv  Detail & Related papers  (2020-12-10T14:17:18Z)
• Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data [61.789797281676606]
  We propose a novel meta-learning latent variable approach, called MetaBridge. It can learn transferable knowledge from a subset of categories with limited labeled data. It can capture the uncertainty of never-seen categories with unlabeled data.
  arXiv  Detail & Related papers  (2020-06-15T21:31:05Z)
• Interpretable Deep Learning Model for Online Multi-touch Attribution [14.62385029537631]
  We propose a novel model called DeepMTA, which combines deep learning model and additive feature explanation model for interpretable online multi-touch attribution. As the first interpretable deep learning model for MTA, DeepMTA considers three important features in the customer journey. Evaluation on a real dataset shows the proposed conversion prediction model achieves 91% accuracy.
  arXiv  Detail & Related papers  (2020-03-26T23:21:40Z)

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.