# The Potential of AutoML for Recommender Systems

arXiv: https://arxiv.org/abs/2402.04453 · HTML: https://mlsanity.com/paper/2402.04453/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 56 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 23 of 23)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://mlsanity.com/paper/1802.03494/) (2018)
- [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](https://mlsanity.com/paper/2010.14925/) (2020)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [Deep Learning based Recommender System: A Survey and New Perspectives](https://mlsanity.com/paper/1707.07435/) (2017)
- [FLAML: A Fast and Lightweight AutoML Library](https://mlsanity.com/paper/1911.04706/) (2019)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](https://mlsanity.com/paper/1603.06212/) (2016)
- [Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL](https://mlsanity.com/paper/2006.13799/) (2020)
- [Generating Personalized Recipes from Historical User Preferences](https://mlsanity.com/paper/1909.00105/) (2019)
- [OBOE: Collaborative Filtering for AutoML Model Selection](https://mlsanity.com/paper/1808.03233/) (2018)
- [AutoDispNet: Improving Disparity Estimation With AutoML](https://mlsanity.com/paper/1905.07443/) (2019)
- [AutoML for Deep Recommender Systems: A Survey](https://mlsanity.com/paper/2203.13922/) (2022)
- [ktrain: A Low-Code Library for Augmented Machine Learning](https://mlsanity.com/paper/2004.10703/) (2020)
- [Benchmarking Automatic Machine Learning Frameworks](https://mlsanity.com/paper/1808.06492/) (2018)
- [LensKit for Python: Next-Generation Software for Recommender System Experiments](https://mlsanity.com/paper/1809.03125/) (2018)
- [An AutoML-based Approach to Multimodal Image Sentiment Analysis](https://mlsanity.com/paper/2102.08092/) (2021)
- [Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization](https://mlsanity.com/paper/2008.13532/) (2020)
- [AutoRec: An Automated Recommender System](https://mlsanity.com/paper/2007.07224/) (2020)
- [Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark](https://mlsanity.com/paper/2009.01564/) (2020)

## Built on by (most cited first, 3 of 3)

- [Algorithm Selection for Recommender Systems via Meta-Learning on Algorithm Characteristics](https://mlsanity.com/paper/2508.04419/) (2025)
- [From AutoRecSys to AutoRecLab: A Call to Build, Evaluate, and Govern Autonomous Recommender-Systems Research Labs](https://mlsanity.com/paper/2510.18104/) (2025)
- [Ensembles at Any Cost? Accuracy-Energy Trade-offs in Recommender Systems](https://mlsanity.com/paper/2604.07869/) (2026)

## Abstract (arXiv)

Automated Machine Learning (AutoML) has greatly advanced applications of Machine Learning (ML) including model compression, machine translation, and computer vision. Recommender Systems (RecSys) can be seen as an application of ML. Yet, AutoML has found little attention in the RecSys community; nor has RecSys found notable attention in the AutoML community. Only few and relatively simple Automated Recommender Systems (AutoRecSys) libraries exist that adopt AutoML techniques. However, these libraries are based on student projects and do not offer the features and thorough development of AutoML libraries. We set out to determine how AutoML libraries perform in the scenario of an inexperienced user who wants to implement a recommender system. We compared the predictive performance of 60 AutoML, AutoRecSys, ML, and RecSys algorithms from 15 libraries, including a mean predictor baseline, on 14 explicit feedback RecSys datasets. To simulate the perspective of an inexperienced user, the algorithms were evaluated with default hyperparameters. We found that AutoML and AutoRecSys libraries performed best. AutoML libraries performed best for six of the 14 datasets (43%), but it was not always the same AutoML library performing best. The single-best library was the AutoRecSys library Auto-Surprise, which performed best on five datasets (36%). On three datasets (21%), AutoML libraries performed poorly, and RecSys libraries with default parameters performed best. Although, while obtaining 50% of all placements in the top five per dataset, RecSys algorithms fall behind AutoML on average. ML algorithms generally performed the worst.