# FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

arXiv: https://arxiv.org/abs/1905.09433 · HTML: https://mlsanity.com/paper/1905.09433/

## Key facts

- Cited by 83 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

## Built on by (most cited first, 30 of 83)

- [Deep Learning for Click-Through Rate Estimation](https://mlsanity.com/paper/2104.10584/) (2021)
- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (2022)
- [ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction](https://mlsanity.com/paper/2310.09234/) (2023)
- [Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph](https://mlsanity.com/paper/2402.13750/) (2024)
- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [CL4CTR: A Contrastive Learning Framework for CTR Prediction](https://mlsanity.com/paper/2212.00522/) (2022)
- [Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation](https://mlsanity.com/paper/2311.18213/) (2023)
- [Ads Recommendation in a Collapsed and Entangled World](https://mlsanity.com/paper/2403.00793/) (2024)
- [ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table](https://mlsanity.com/paper/2104.08542/) (2021)
- [DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction](https://mlsanity.com/paper/2203.11014/) (2022)
- [APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2203.16218/) (2022)
- [MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction](https://mlsanity.com/paper/2308.01737/) (2023)
- [ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems](https://mlsanity.com/paper/2403.12660/) (2024)
- [FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction](https://mlsanity.com/paper/2209.05016/) (2022)
- [Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction](https://mlsanity.com/paper/2105.07752/) (2021)
- [LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding](https://mlsanity.com/paper/2408.11523/) (2024)
- [Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model](https://mlsanity.com/paper/2505.19505/) (2025)
- [HSR: Hyperbolic Social Recommender](https://mlsanity.com/paper/2102.09389/) (2021)
- [Boost CTR Prediction for New Advertisements via Modeling Visual Content](https://mlsanity.com/paper/2209.11727/) (2022)
- [Deep Task-specific Bottom Representation Network for Multi-Task Recommendation](https://mlsanity.com/paper/2308.05996/) (2023)
- [Deep Tree Tensor Networks](https://mlsanity.com/paper/2502.09928/) (2025)
- [Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000× Compression and 3.1× Faster Inference](https://mlsanity.com/paper/2108.02191/) (2021)
- [A Comprehensive Survey on Automated Machine Learning for Recommendations](https://mlsanity.com/paper/2204.01390/) (2022)
- [Deep Ensemble Shape Calibration: Multi-Field Post-hoc Calibration in Online Advertising](https://mlsanity.com/paper/2401.09507/) (2024)
- [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)
- [MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization](https://mlsanity.com/paper/2506.07563/) (2025)
- [MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation](https://mlsanity.com/paper/2108.07505/) (2021)
- [MACEst: The reliable and trustworthy Model Agnostic Confidence Estimator](https://mlsanity.com/paper/2109.01531/) (2021)
- [Boosting Factorization Machines via Saliency-Guided Mixup](https://mlsanity.com/paper/2206.08661/) (2022)

## Abstract (arXiv)

Advertising and feed ranking are essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising and feed ranking systems, click through rate (CTR) prediction plays a central role. There are many proposed models in this field such as logistic regression, tree based models, factorization machine based models and deep learning based CTR models. However, many current works calculate the feature interactions in a simple way such as Hadamard product and inner product and they care less about the importance of features. In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions. On the one hand, the FiBiNET can dynamically learn the importance of features via the Squeeze-Excitation network (SENET) mechanism; on the other hand, it is able to effectively learn the feature interactions via bilinear function. We conduct extensive experiments on two real-world datasets and show that our shallow model outperforms other shallow models such as factorization machine(FM) and field-aware factorization machine(FFM). In order to improve performance further, we combine a classical deep neural network(DNN) component with the shallow model to be a deep model. The deep FiBiNET consistently outperforms the other state-of-the-art deep models such as DeepFM and extreme deep factorization machine(XdeepFM).