AutoIAS: Automatic Integrated Architecture Searcher for Click-Trough Rate Prediction
Abstract
Automating architecture design for recommendation tasks becomes a trending topic because expert efforts are saved, and better performance is expected. Neural Architecture Search (NAS) is introduced to discover powerful CTR prediction model architectures in recent works. CTR prediction model usually consists of three components: embedding layer, interaction layer, and deep neural network. However, existing automation works focus on searching single component and leaving other components hand-crafted. The isolated searching will cause incompatibility among components and lead to weak generalization ability. Moreover, there is not a unified framework for integrated CTR prediction model architecture searching. This paper presents Automatic Integrated Architecture Searcher (AutoIAS), a framework that provides a practical and general method to find optimal CTR prediction model architecture in an automatic manner. In AutoIAS, we unify existing interaction-based CTR prediction model architectures and propose an integrated search space for a complete CTR prediction model. We utilize a supernet to predict the performance of sub-architectures, and the supernet is trained with Knowledge Distillation(KD) to enhance consistency among sub-architectures. To efficiently explore the search space, we design an architecture generator network that explicitly models the architecture dependencies among components and generates conditioned architectures distribution for each component. Experiments on public datasets show the outstanding performance and generalization ability of AutoIAS. Ablation study shows the effectiveness of the KD-based supernet training method and the Architecture Generator Network.
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The search strategy shifts from discrete reinforcement learning to differentiable searching, which adaptively selects architectures within one-shot learning to circumvent computational burden, for more efficient convergence.
“The search strategy shifts from the discrete reinforcement learning process, which iteratively samples architectures for training and is time-consuming, into the differentiable searching, which adaptively selects architectures within one-shot learning to circumvent the computational burden, for more efficient convergence.”
From When large language models meet personalization: perspectives of challenges and opportunities (2024) · cites this paper as [191]
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- Authors
- Wei, Z., Wang, X., & Zhu, W.
- Affiliations
- Tsinghua University, Beijing, China
- Tsinghua University (CN)
- ORCID
- Venue
- Proceedings of the 30th ACM International Conference on Information & Knowledge Management
- Publisher
- ACM
- Published
- 2021-10-26
- Pages
- 2101-2110
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W3208642157
- Cited by
- 13
- References
- 25
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 4 · 2022: 3
- FWCI
- 2.07
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion)
- Funders
- Tsinghua GuoQiang Research Center (2020GQG1014)
- National Natural Science Foundation of China (62050110)
- National Key Research and Development Program of China (2020AAA0106301)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- neural architecture search, click-through rate prediction, recommender systems, supernet, knowledge distillation, embedding layer, interaction layer, search space design
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