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Field-aware Factorization Machines in a Real-world Online Advertising System

arXiv 1701.04099 ↗First posted 15 Jan 2017cs.LGcited by 21

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Abstract (arXiv)

Predicting user response is one of the core machine learning tasks in computational advertising. Field-aware Factorization Machines (FFM) have recently been established as a state-of-the-art method for that problem and in particular won two Kaggle challenges. This paper presents some results from implementing this method in a production system that predicts click-through and conversion rates for display advertising and shows that this method it is not only effective to win challenges but is also valuable in a real-world prediction system. We also discuss some specific challenges and solutions to reduce the training time, namely the use of an innovative seeding algorithm and a distributed learning mechanism.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jan 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 7; 2023: 5; 2024: 2; 2025: 0; 2026: 2.

21 papers.

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  1. 2021FM2: Field-matrixed Factorization Machines for Recommender Systemscited by 31
  2. 2022Click-Through Rate Prediction in Online Advertising: A Literature Reviewcited by 31
  3. 2021User Response Prediction in Online Advertisingcited by 15
  4. 2021Multi-Factors Aware Dual-Attentional Knowledge Tracingcited by 12
  5. 2022DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Predictioncited by 12
  6. 2021Online Strongly Convex Optimization with Unknown Delayscited by 11
  7. 2023Causality-based CTR Prediction using Graph Neural Networkscited by 9
  8. 2023Temporal Interest Network for User Response Predictioncited by 9
  9. 2022An Introduction to Matrix factorization and Factorization Machines in Recommendation System, and Beyondcited by 5
  10. 2024Improved Online Learning Algorithms for CTR Prediction in Ad Auctionscited by 2
  11. 2022Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Predictioncited by 1
  12. 2023Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctionscited by 1
9 more
  1. 2024A Bag of Tricks for Scaling CPU-based Deep FFMs to more than 300m Predictions per Secondcited by 1
  2. 2021Online Interaction Detection for Click-Through Rate Predictioncited by 0
  3. 2022Multi-Head Online Learning for Delayed Feedback Modelingcited by 0
  4. 2022Risk-Aware Bid Optimization for Online Display Advertisementcited by 0
  5. 2022Conversion-Based Dynamic-Creative-Optimization in Native Advertisingcited by 0
  6. 2023Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectivescited by 0
  7. 2023Unbiased Filtering Of Accidental Clicks in Verizon Media Native Advertisingcited by 0
  8. 2026tensorFM: Low-Rank Approximations of Cross-Order Feature Interactionscited by 0
  9. 2026Deferred is Better: A Framework for Multi-Granularity Deferred Interaction of Heterogeneous Featurescited by 0