@article{10.1145/3630104,
author = {Chen, Bo and Zhao, Xiangyu and Wang, Yejing and Fan, Wenqi and Guo, Huifeng and Tang, Ruiming},
title = {A Comprehensive Survey on Automated Machine Learning for Recommendations},
year = {2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3630104},
doi = {10.1145/3630104},
abstract = {Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user’s interests and preferences. They have unprecedented feature representations effectiveness and the capacity of modeling the non-linear relationships between users and items. Despite their advancements, DRS models, like other deep learning models, employ sophisticated neural network architectures and other vital components that are typically designed and tuned by human experts. This article will give a comprehensive summary of automated machine learning (AutoML) for developing DRS models. We first provide an overview of AutoML for DRS models and the related techniques. Then we discuss the state-of-the-art AutoML approaches that automate the feature selection, feature embeddings, feature interactions, and model training in DRS. We point out that the existing AutoML-based recommender systems are developing to a multi-component joint search with abstract search space and efficient search algorithm. Finally, we discuss appealing research directions and summarize the survey.},
note = {Just Accepted},
journal = {ACM Trans. Recomm. Syst.},
month = {oct},
keywords = {Recommender Systems, Automated Machine Learning, Neural networks}
}