mlsanityData to 14 Oct 2026

User Response Prediction in Online Advertising

arXiv 2101.02342 ↗First posted 7 Jan 2021cs.SI cs.LGcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 99 of its 205 references that are in the map. First posted on arXiv 7 Jan 2021.

Builds on

99 of 205 references.

References · 99

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38 more
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50 most cited of 99 All 99 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 4; 2024: 1; 2025: 4; 2026: 2.

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  1. 2022Knowledge Graph Contrastive Learning for Recommendationcited by 70
  2. 2022Click-Through Rate Prediction in Online Advertising: A Literature Reviewcited by 31
  3. 2023Knowledge Enhancement for Contrastive Multi-Behavior Recommendationcited by 16
  4. 2022A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacousticcited by 7
  5. 2025KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendationcited by 3
  6. 2026Rank-Learner: Orthogonal Ranking of Treatment Effectscited by 3
  7. 2023A Scalable and Efficient Iterative Method for Copying Machine Learning Classifierscited by 1
  8. 2023Weighted Multi-Level Feature Factorization for App ads CTR and installation predictioncited by 1
  9. 2023Impression-Aware Recommender Systemscited by 1
  10. 2024Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Predictioncited by 1
  11. 2025Optimizing Storage Overhead of User Behavior Log for ML-embedded Mobile Appscited by 1
  12. 2021Machines & Influence: An Information Systems Lenscited by 0
3 more
  1. 2025Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directionscited by 0
  2. 2025A Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System for Advertisement Retrieval and Personalizationcited by 0
  3. 2026Decoding the Hook: A Multimodal LLM Framework for Analyzing the Hooking Period of Video Adscited by 0