# AutoField: Automating Feature Selection in Deep Recommender Systems

arXiv: https://arxiv.org/abs/2204.09078 · HTML: https://mlsanity.com/paper/2204.09078/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 51 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 21 of 21)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [Deep Learning based Recommender System: A Survey and New Perspectives](https://mlsanity.com/paper/1707.07435/) (2017)
- [Automated Machine Learning: From Principles to Practices](https://mlsanity.com/paper/1810.13306/) (2018)
- [AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://mlsanity.com/paper/2003.11235/) (2020)
- [AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations](https://mlsanity.com/paper/2002.11252/) (2020)
- [AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications](https://mlsanity.com/paper/1904.12857/) (2019)
- [Security and Privacy on Blockchain](https://mlsanity.com/paper/1903.07602/) (2019)
- [AutoLoss: Automated Loss Function Search in Recommendations](https://mlsanity.com/paper/2106.06713/) (2021)
- [AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning](https://mlsanity.com/paper/2008.12001/) (2020)
- [Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent](https://mlsanity.com/paper/2009.09230/) (2020)
- [NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation](https://mlsanity.com/paper/1809.00082/) (2018)

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

- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [Diffusion Augmentation for Sequential Recommendation](https://mlsanity.com/paper/2309.12858/) (2023)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [IMF: Interactive Multimodal Fusion Model for Link Prediction](https://mlsanity.com/paper/2303.10816/) (2023)
- [Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation](https://mlsanity.com/paper/2311.13184/) (2023)
- [Experimental Analysis of Large-scale Learnable Vector Storage Compression](https://mlsanity.com/paper/2311.15578/) (2023)
- [MvFS: Multi-view Feature Selection for Recommender System](https://mlsanity.com/paper/2309.02064/) (2023)
- [Large Language Model Enhanced Recommender Systems: A Survey](https://mlsanity.com/paper/2412.13432/) (2024)
- [ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems](https://mlsanity.com/paper/2403.12660/) (2024)
- [Put Teacher in Student's Shoes: Cross-Distillation for Ultra-compact Model Compression Framework](https://mlsanity.com/paper/2507.04636/) (2025)
- [A Versatile Graph Learning Approach through LLM-based Agent](https://mlsanity.com/paper/2309.04565/) (2023)
- [RRADistill: Distilling LLMs' Passage Ranking Ability for Long-Tail Queries Document Re-Ranking on a Search Engine](https://mlsanity.com/paper/2410.18097/) (2024)
- [Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks](https://mlsanity.com/paper/2311.06028/) (2023)
- [Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems](https://mlsanity.com/paper/2312.12750/) (2023)
- [DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems](https://mlsanity.com/paper/2402.00390/) (2024)
- [Retrievable Domain-Sensitive Feature Memory for Multi-Domain Recommendation](https://mlsanity.com/paper/2405.12892/) (2024)
- [Boosting Factorization Machines via Saliency-Guided Mixup](https://mlsanity.com/paper/2206.08661/) (2022)
- [GPRec: Bi-level User Modeling for Deep Recommenders](https://mlsanity.com/paper/2410.20730/) (2024)
- [GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks](https://mlsanity.com/paper/2506.16114/) (2025)
- [RecHarness: A Bandit-Routed Agentic Harness for Self-Evolving Recommender Systems](https://mlsanity.com/paper/2607.29241/) (2026)
- [TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems](https://mlsanity.com/paper/2507.03895/) (2025)
- [How Usable is Automated Feature Engineering for Tabular Data?](https://mlsanity.com/paper/2508.13932/) (2025)
- [Performance-Driven QUBO for Recommender Systems on Quantum Annealers](https://mlsanity.com/paper/2410.15272/) (2024)
- [ShuffleGate: Scalable Feature Optimization for Recommender Systems via Batch-wise Sensitivity Learning](https://mlsanity.com/paper/2503.09315/) (2025)
- [IRA: Adaptive Interest-aware Representation and Alignment for Personalized Multi-interest Retrieval](https://mlsanity.com/paper/2504.17529/) (2025)
- [DimGrow: Memory-Efficient Field-level Embedding Dimension Search](https://mlsanity.com/paper/2505.12683/) (2025)
- [FairFS: Addressing Deep Feature Selection Biases for Recommender System](https://mlsanity.com/paper/2602.20001/) (2026)
- [LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems](https://mlsanity.com/paper/2606.01111/) (2026)
- [Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search](https://mlsanity.com/paper/2607.01387/) (2026)
- [Profiling What Matters: Context-Aware Item Profiles from Large-Scale Metadata for LLM Recommenders](https://mlsanity.com/paper/2608.20801/) (2026)

## Abstract (arXiv)

Feature quality has an impactful effect on recommendation performance. Thereby, feature selection is a critical process in developing deep learning-based recommender systems. Most existing deep recommender systems, however, focus on designing sophisticated neural networks, while neglecting the feature selection process. Typically, they just feed all possible features into their proposed deep architectures, or select important features manually by human experts. The former leads to non-trivial embedding parameters and extra inference time, while the latter requires plenty of expert knowledge and human labor effort. In this work, we propose an AutoML framework that can adaptively select the essential feature fields in an automatic manner. Specifically, we first design a differentiable controller network, which is capable of automatically adjusting the probability of selecting a particular feature field; then, only selected feature fields are utilized to retrain the deep recommendation model. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our framework. We conduct further experiments to investigate its properties, including the transferability, key components, and parameter sensitivity.