Towards Automated Model Design on Recommender Systems Article Swipe
Tunhou Zhang
,
Dehua Cheng
,
Yuchen He
,
Zhengxing Chen
,
Xiaoliang Dai
,
Liang Xiong
,
Yudong Liu
,
Feng Cheng
,
Yufan Cao
,
Feng Yan
,
Hai Li
,
Yiran Chen
,
Wei Wen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3706124
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3706124
The increasing popularity of deep learning models has created new opportunities for developing artificial intelligence–based recommender systems. Designing recommender systems using deep neural networks (DNNs) requires careful architecture design, and further optimization demands extensive co-design efforts on jointly optimizing model architecture and hardware. Design automation, such as Automated Machine Learning (AutoML), is necessary to fully exploit the potential of recommender model design, including model choices and model–hardware co-design strategies. We introduce a novel paradigm that utilizes weight sharing to explore abundant solution spaces. Our paradigm creates a large supernet to search for optimal architectures and co-design strategies to address the challenges of data multimodality and heterogeneity in the recommendation domain. From a model perspective, the supernet includes a variety of operators, dense connectivity, and dimension search options. From a co-design perspective, it encompasses versatile Processing-In-Memory (PIM) configurations to produce hardware-efficient models. Our solution space’s scale, heterogeneity, and complexity pose several challenges, which we address by proposing various techniques for training and evaluating the supernet. Our crafted models show promising results on three Click-Through Rate (CTR) prediction benchmarks, outperforming both manually designed and AutoML-crafted models with state-of-the-art performance when focusing solely on architecture search. From a co-design perspective, we achieve 2× floating-point operations efficiency, 1.8× energy efficiency, and 1.5× performance improvements in recommender models.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3706124
- https://dl.acm.org/doi/pdf/10.1145/3706124
- OA Status
- hybrid
- Cited By
- 1
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405195359
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405195359Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3706124Digital Object Identifier
- Title
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Towards Automated Model Design on Recommender SystemsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-12-09Full publication date if available
- Authors
-
Tunhou Zhang, Dehua Cheng, Yuchen He, Zhengxing Chen, Xiaoliang Dai, Liang Xiong, Yudong Liu, Feng Cheng, Yufan Cao, Feng Yan, Hai Li, Yiran Chen, Wei WenList of authors in order
- Landing page
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https://doi.org/10.1145/3706124Publisher landing page
- PDF URL
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https://dl.acm.org/doi/pdf/10.1145/3706124Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3706124Direct OA link when available
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Computer science, Recommender system, Exploit, Artificial intelligence, Machine learning, Popularity, Deep learning, Automation, Architecture, Data science, Social psychology, Mechanical engineering, Art, Visual arts, Engineering, Computer security, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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