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.
@article{arxiv.2007.07224,
title = {AutoRec: An Automated Recommender System},
author = {Ting-Hsiang Wang and Qingquan Song and Xiaotian Han and Zirui Liu and Haifeng Jin and Xia Hu},
journal= {arXiv preprint arXiv:2007.07224},
year = {2020}
}