# AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

arXiv: https://arxiv.org/abs/2003.11235 · HTML: https://mlsanity.com/paper/2003.11235/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 30 of 52)

- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [Deep Learning for Click-Through Rate Estimation](https://mlsanity.com/paper/2104.10584/) (2021)
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- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (2022)
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- [TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction](https://mlsanity.com/paper/2108.09127/) (2021)
- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [GraphFM: Graph Factorization Machines for Feature Interaction Modeling](https://mlsanity.com/paper/2105.11866/) (2021)
- [Memorize, Factorize, or be Na"ive: Learning Optimal Feature Interaction Methods for CTR Prediction](https://mlsanity.com/paper/2108.01265/) (2021)
- [DropNAS: Grouped Operation Dropout for Differentiable Architecture Search](https://mlsanity.com/paper/2201.11679/) (2022)
- [DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction](https://mlsanity.com/paper/2203.11014/) (2022)
- [Physics-Guided Foundation Model for Scientific Discovery: An Application to Aquatic Science](https://mlsanity.com/paper/2502.06084/) (2025)
- [COPR: Consistency-Oriented Pre-Ranking for Online Advertising](https://mlsanity.com/paper/2306.03516/) (2023)
- [MvFS: Multi-view Feature Selection for Recommender System](https://mlsanity.com/paper/2309.02064/) (2023)
- [Neural Graph Matching based Collaborative Filtering](https://mlsanity.com/paper/2105.04067/) (2021)
- [AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems](https://mlsanity.com/paper/2606.26859/) (2026)
- [TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets](https://mlsanity.com/paper/2204.07615/) (2022)
- [Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems](https://mlsanity.com/paper/2206.13764/) (2022)
- [DCAP: Deep Cross Attentional Product Network for User Response Prediction](https://mlsanity.com/paper/2105.08649/) (2021)
- [i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender Systems](https://mlsanity.com/paper/2204.00281/) (2022)
- [A Comprehensive Survey on Automated Machine Learning for Recommendations](https://mlsanity.com/paper/2204.01390/) (2022)
- [ColdNAS: Search to Modulate for User Cold-Start Recommendation](https://mlsanity.com/paper/2306.03387/) (2023)
- [AutoML for Large Capacity Modeling of Meta's Ranking Systems](https://mlsanity.com/paper/2311.07870/) (2023)
- [PANTHER: Generative Pretraining Beyond Language for Sequential User Behavior Modeling](https://mlsanity.com/paper/2510.10102/) (2025)
- [Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering](https://mlsanity.com/paper/2307.11004/) (2023)
- [Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks](https://mlsanity.com/paper/2308.14902/) (2023)
- [Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale](https://mlsanity.com/paper/2311.08430/) (2023)
- [Evolution-based Feature Selection for Predicting Dissolved Oxygen Concentrations in Lakes](https://mlsanity.com/paper/2403.18923/) (2024)
- [Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models](https://mlsanity.com/paper/2411.15731/) (2024)
- [AEFE: Automatic Embedded Feature Engineering for Categorical Features](https://mlsanity.com/paper/2110.09770/) (2021)

## Abstract (arXiv)

Learning feature interactions is crucial for click-through rate (CTR) prediction in recommender systems. In most existing deep learning models, feature interactions are either manually designed or simply enumerated. However, enumerating all feature interactions brings large memory and computation cost. Even worse, useless interactions may introduce noise and complicate the training process. In this work, we propose a two-stage algorithm called Automatic Feature Interaction Selection (AutoFIS). AutoFIS can automatically identify important feature interactions for factorization models with computational cost just equivalent to training the target model to convergence. In the search stage, instead of searching over a discrete set of candidate feature interactions, we relax the choices to be continuous by introducing the architecture parameters. By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model. In the re-train stage, we keep the architecture parameters serving as an attention unit to further boost the performance. Offline experiments on three large-scale datasets (two public benchmarks, one private) demonstrate that AutoFIS can significantly improve various FM based models. AutoFIS has been deployed onto the training platform of Huawei App Store recommendation service, where a 10-day online A/B test demonstrated that AutoFIS improved the DeepFM model by 20.3% and 20.1% in terms of CTR and CVR respectively.