# Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising

arXiv: https://arxiv.org/abs/1806.03514 · HTML: https://mlsanity.com/paper/1806.03514/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2018.

## Built on by (most cited first, 30 of 59)

- [End-to-End User Behavior Retrieval in Click-Through RatePrediction Model](https://mlsanity.com/paper/2108.04468/) (2021)
- [Deep Learning for Click-Through Rate Estimation](https://mlsanity.com/paper/2104.10584/) (2021)
- [FM2: Field-matrixed Factorization Machines for Recommender Systems](https://mlsanity.com/paper/2102.12994/) (2021)
- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (2022)
- [On the Embedding Collapse when Scaling up Recommendation Models](https://mlsanity.com/paper/2310.04400/) (2023)
- [STEM: Unleashing the Power of Embeddings for Multi-task Recommendation](https://mlsanity.com/paper/2308.13537/) (2023)
- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions](https://mlsanity.com/paper/2107.06650/) (2021)
- [CL4CTR: A Contrastive Learning Framework for CTR Prediction](https://mlsanity.com/paper/2212.00522/) (2022)
- [Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction](https://mlsanity.com/paper/2105.08909/) (2021)
- [Ads Recommendation in a Collapsed and Entangled World](https://mlsanity.com/paper/2403.00793/) (2024)
- [Memorize, Factorize, or be Na"ive: Learning Optimal Feature Interaction Methods for CTR Prediction](https://mlsanity.com/paper/2108.01265/) (2021)
- [Causality-based CTR Prediction using Graph Neural Networks](https://mlsanity.com/paper/2301.12762/) (2023)
- [Temporal Interest Network for User Response Prediction](https://mlsanity.com/paper/2308.08487/) (2023)
- [Joint Online Learning and Decision-making via Dual Mirror Descent](https://mlsanity.com/paper/2104.09750/) (2021)
- [HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer](https://mlsanity.com/paper/2305.17386/) (2023)
- [DCAP: Deep Cross Attentional Product Network for User Response Prediction](https://mlsanity.com/paper/2105.08649/) (2021)
- [Looking at CTR Prediction Again: Is Attention All You Need?](https://mlsanity.com/paper/2105.05563/) (2021)
- [ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising](https://mlsanity.com/paper/2406.10517/) (2024)
- [Field Matters: A Lightweight LLM-enhanced Method for CTR Prediction](https://mlsanity.com/paper/2505.14057/) (2025)
- [MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising](https://mlsanity.com/paper/2403.02607/) (2024)
- [Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction](https://mlsanity.com/paper/2505.17999/) (2025)
- [From Scaling to Structured Expressivity: Rethinking Transformers for CTR Prediction](https://mlsanity.com/paper/2511.12081/) (2025)
- [TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds](https://mlsanity.com/paper/2604.13737/) (2026)
- [AEFE: Automatic Embedded Feature Engineering for Categorical Features](https://mlsanity.com/paper/2110.09770/) (2021)
- [AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling](https://mlsanity.com/paper/2210.15154/) (2022)
- [MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction](https://mlsanity.com/paper/2211.01334/) (2022)
- [Exploiting Field Dependencies for Learning on Categorical Data](https://mlsanity.com/paper/2307.09321/) (2023)
- [Low Rank Field-Weighted Factorization Machines for Low Latency Item Recommendation](https://mlsanity.com/paper/2408.00801/) (2024)
- [DashCLIP: Leveraging multimodal models for generating semantic embeddings for DoorDash](https://mlsanity.com/paper/2504.07110/) (2025)

## Abstract (arXiv)

Click-through rate (CTR) prediction is a critical task in online display advertising. The data involved in CTR prediction are typically multi-field categorical data, i.e., every feature is categorical and belongs to one and only one field. One of the interesting characteristics of such data is that features from one field often interact differently with features from different other fields. Recently, Field-aware Factorization Machines (FFMs) have been among the best performing models for CTR prediction by explicitly modeling such difference. However, the number of parameters in FFMs is in the order of feature number times field number, which is unacceptable in the real-world production systems. In this paper, we propose Field-weighted Factorization Machines (FwFMs) to model the different feature interactions between different fields in a much more memory-efficient way. Our experimental evaluations show that FwFMs can achieve competitive prediction performance with only as few as 4% parameters of FFMs. When using the same number of parameters, FwFMs can bring 0.92% and 0.47% AUC lift over FFMs on two real CTR prediction data sets.