Automated Machine Learning for Deep Recommender Systems: A Survey
- URL: http://arxiv.org/abs/2204.01390v1
- Date: Mon, 4 Apr 2022 11:17:43 GMT
- Title: Automated Machine Learning for Deep Recommender Systems: A Survey
- Authors: Bo Chen, Xiangyu Zhao, Yejing Wang, Wenqi Fan, Huifeng Guo, Ruiming
Tang
- Abstract summary: 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 system design in DRS.
- Score: 25.942427065983754
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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 system design
in DRS. Finally, we discuss appealing research directions and summarize the
survey.
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