AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System
Open accessAbstract
Recently, deep learning models have been widely explored in recommender systems. Though having achieved remarkable success, the design of task-aware recommendation models usually requires manual feature engineering and architecture engineering from domain experts. To relieve those efforts, we explore the potential of neural architecture search (NAS) and introduce AMEIR for Automatic behavior Modeling, interaction Exploration and multi-layer perceptron (MLP) Investigation in the Recommender system. Specifically, AMEIR divides the complete recommendation models into three stages of behavior modeling, interaction exploration, MLP aggregation, and introduces a novel search space containing three tailored subspaces that cover most of the existing methods and thus allow for searching better models. To find the ideal architecture efficiently and effectively, AMEIR realizes the one-shot random search in recommendation progressively on the three stages and assembles the search results as the final outcome. The experiment over various scenarios reveals that AMEIR outperforms competitive baselines of elaborate manual design and leading algorithmic complex NAS methods with lower model complexity and comparable time cost, indicating efficacy, efficiency, and robustness of the proposed method.
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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 [190]
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When large language models meet personalization: perspectives of challenges and opportunities
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“Model architecture search, like [170], [171], [172], [173], expands the search space to the integral architectures.”
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Cross Feature Engineering for Anti-Fraud Task in Insurance
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- Authors
- Zhao, P., Xiao, K., Zhang, Y., Bian, K., & Yan, W.
- Affiliations
- Peking University (CN)
- Alibaba Group (China) (CN)
- Venue
- Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
- Published
- 2021-08
- Pages
- 2104-2110
- OpenAlex
- W3190884738
- Cited by
- 7
- References
- 75
- Citations by year
- 2024: 4 · 2023: 1 · 2022: 2
- FWCI
- 1.45
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61632017, 62032003)
- National Key Research and Development Program of China (2017YFB0803302)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- neural architecture search, recommender systems, behavioral modeling, search space design, model reduction
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