# AutoML for Deep Recommender Systems: A Survey

arXiv: https://arxiv.org/abs/2203.13922 · HTML: https://mlsanity.com/paper/2203.13922/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2022.

## Built on by (most cited first, 29 of 29)

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Graph Condensation for Inductive Node Representation Learning](https://mlsanity.com/paper/2307.15967/) (2023)
- [Graph Condensation: A Survey](https://mlsanity.com/paper/2401.11720/) (2024)
- [Semi-decentralized Federated Ego Graph Learning for Recommendation](https://mlsanity.com/paper/2302.10900/) (2023)
- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [Are Large Language Models the New Interface for Data Pipelines?](https://mlsanity.com/paper/2406.06596/) (2024)
- [Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback](https://mlsanity.com/paper/2602.12612/) (2026)
- [ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems](https://mlsanity.com/paper/2403.12660/) (2024)
- [Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation](https://mlsanity.com/paper/2310.10998/) (2023)
- [OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction](https://mlsanity.com/paper/2408.08585/) (2024)
- [Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation](https://mlsanity.com/paper/2402.02855/) (2024)
- [Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion](https://mlsanity.com/paper/2312.15826/) (2023)
- [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)
- [Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering](https://mlsanity.com/paper/2307.11004/) (2023)
- [The Potential of AutoML for Recommender Systems](https://mlsanity.com/paper/2402.04453/) (2024)
- [RecHarness: A Bandit-Routed Agentic Harness for Self-Evolving Recommender Systems](https://mlsanity.com/paper/2607.29241/) (2026)
- [Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems](https://mlsanity.com/paper/2401.04408/) (2024)
- [Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models](https://mlsanity.com/paper/2402.11641/) (2024)
- [Graph Federated Learning for Personalized Privacy Recommendation](https://mlsanity.com/paper/2508.06208/) (2025)
- [OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking](https://mlsanity.com/paper/2309.01552/) (2023)
- [DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops](https://mlsanity.com/paper/2311.05864/) (2023)
- [Hypergraph-Enhanced Dual Convolutional Network for Bundle Recommendation](https://mlsanity.com/paper/2312.11018/) (2023)
- [AOTree: Aspect Order Tree-based Model for Explainable Recommendation](https://mlsanity.com/paper/2407.19937/) (2024)
- [HyperZero: A Customized End-to-End Auto-Tuning System for Recommendation with Hourly Feedback](https://mlsanity.com/paper/2501.18126/) (2025)
- [Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks](https://mlsanity.com/paper/2506.03391/) (2025)
- [Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance](https://mlsanity.com/paper/2506.23314/) (2025)
- [A Framework for Assessing AI Agent Decisions and Outcomes in AutoML Pipelines](https://mlsanity.com/paper/2602.22442/) (2026)
- [LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems](https://mlsanity.com/paper/2606.01111/) (2026)

## Abstract (arXiv)

Recommender systems play a significant role in information filtering and have been utilized in different scenarios, such as e-commerce and social media. With the prosperity of deep learning, deep recommender systems show superior performance by capturing non-linear information and item-user relationships. However, the design of deep recommender systems heavily relies on human experiences and expert knowledge. To tackle this problem, Automated Machine Learning (AutoML) is introduced to automatically search for the proper candidates for different parts of deep recommender systems. This survey performs a comprehensive review of the literature in this field. Firstly, we propose an abstract concept for AutoML for deep recommender systems (AutoRecSys) that describes its building blocks and distinguishes it from conventional AutoML techniques and recommender systems. Secondly, we present a taxonomy as a classification framework containing feature selection search, embedding dimension search, feature interaction search, model architecture search, and other components search. Furthermore, we put a particular emphasis on the search space and search strategy, as they are the common thread to connect all methods within each category and enable practitioners to analyze and compare various approaches. Finally, we propose four future promising research directions that will lead this line of research.