User Response Prediction in Online Advertising

Abstract

Online advertising, as the vast market, has gained significant attention in various platforms ranging from search engines, third-party websites, social media, and mobile apps. The prosperity of online campaigns is a challenge in online marketing and is usually evaluated by user response through different metrics, such as clicks on advertisement (ad) creatives, subscriptions to products, purchases of items, or explicit user feedback through online surveys. Recent years have witnessed a significant increase in the number of studies using computational approaches, including machine learning methods, for user response prediction. However, existing literature mainly focuses on algorithmic-driven designs to solve specific challenges, and no comprehensive review exists to answer many important questions. What are the parties involved in the online digital advertising eco-systems? What type of data are available for user response prediction? How to predict user response in a reliable and/or transparent way? In this survey, we provide a comprehensive review of user response prediction in online advertising and related recommender applications. Our essential goal is to provide a thorough understanding of online advertising platforms, stakeholders, data availability, and typical ways of user response prediction. We propose a taxonomy to categorize state-of-the-art user response prediction methods, primarily focus on the current progress of machine learning methods used in different online platforms. In addition, we also review applications of user response prediction, benchmark datasets, and open-source codes in the field.

arXiv commentACM Computing Surveys (CSUR), 2021, preprint

References in corpus (44)

• Neural Architecture Search with Reinforcement Learning
• xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
• Deep Learning Recommendation Model for Personalization and Recommendation Systems
• Entity Embeddings of Categorical Variables
• Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
• DKN: Deep Knowledge-Aware Network for News Recommendation
• MLPerf Training Benchmark
• Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
• On Application of Learning to Rank for E-Commerce Search
• Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
• Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
• GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
• On the Difficulty of Evaluating Baselines: A Study on Recommender Systems
• Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems
• Jointly Learning to Recommend and Advertise
• Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
• Higher-Order Factorization Machines
• Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net
• Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising
• DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
• Deep Character-Level Click-Through Rate Prediction for Sponsored Search
• You Must Have Clicked on this Ad by Mistake! Data-Driven Identification of Accidental Clicks on Mobile Ads with Applications to Advertiser Cost Discounting and Click-Through Rate Prediction
• Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation
• FLEN: Leveraging Field for Scalable CTR Prediction
• Click Through Rate Prediction for Contextual Advertisment Using Linear Regression
• PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System
• FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
• Using Neural Networks for Click Prediction of Sponsored Search
• Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
• Field-aware Factorization Machines in a Real-world Online Advertising System
• Did We Get It Right? Predicting Query Performance in E-commerce Search
• Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction
• Personalising Mobile Advertising Based on Users Installed Apps
• Neural Feature Embedding for User Response Prediction in Real-Time Bidding (RTB)
• Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
• Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling
• Time-Aware Prospective Modeling of Users for Online Display Advertising
• Deeply Supervised Semantic Model for Click-Through Rate Prediction in Sponsored Search
• Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate
• Expanding Click and Buy rates: Exploration of evaluation metrics that measure the impact of personalized recommendation engines on e-commerce platforms
• Order Matters at Fanatics Recommending Sequentially Ordered Products by LSTM Embedded with Word2Vec
• Dynamic Hierarchical Empirical Bayes: A Predictive Model Applied to Online Advertising
• Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding
• Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution