# AutoRec: An Automated Recommender System

arXiv: https://arxiv.org/abs/2007.07224 · HTML: https://mlsanity.com/paper/2007.07224/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (2022)
- [A Comprehensive Survey on Automated Machine Learning for Recommendations](https://mlsanity.com/paper/2204.01390/) (2022)
- [On the Generalizability and Predictability of Recommender Systems](https://mlsanity.com/paper/2206.11886/) (2022)
- [The Potential of AutoML for Recommender Systems](https://mlsanity.com/paper/2402.04453/) (2024)
- [Dataset-Agnostic Recommender Systems](https://mlsanity.com/paper/2501.07294/) (2025)
- [Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks](https://mlsanity.com/paper/2506.03391/) (2025)

## Abstract (arXiv)

Realistic recommender systems are often required to adapt to ever-changing data and tasks or to explore different models systematically. To address the need, we present AutoRec, an open-source automated machine learning (AutoML) platform extended from the TensorFlow ecosystem and, to our knowledge, the first framework to leverage AutoML for model search and hyperparameter tuning in deep recommendation models. AutoRec also supports a highly flexible pipeline that accommodates both sparse and dense inputs, rating prediction and click-through rate (CTR) prediction tasks, and an array of recommendation models. Lastly, AutoRec provides a simple, user-friendly API. Experiments conducted on the benchmark datasets reveal AutoRec is reliable and can identify models which resemble the best model without prior knowledge.