DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems Article Swipe
Sheng Zhang
,
Maolin Wang
,
Yao Zhao
,
Chenyi Zhuang
,
Jinjie Gu
,
Ruocheng Guo
,
Xiangyu Zhao
,
Zijian Zhang
,
Hongzhi Yin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.00390
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.00390
In the era of data proliferation, efficiently sifting through vast information to extract meaningful insights has become increasingly crucial. This paper addresses the computational overhead and resource inefficiency prevalent in existing Sequential Recommender Systems (SRSs). We introduce an innovative approach combining pruning methods with advanced model designs. Furthermore, we delve into resource-constrained Neural Architecture Search (NAS), an emerging technique in recommender systems, to optimize models in terms of FLOPs, latency, and energy consumption while maintaining or enhancing accuracy. Our principal contribution is the development of a Data-aware Neural Architecture Search for Recommender System (DNS-Rec). DNS-Rec is specifically designed to tailor compact network architectures for attention-based SRS models, thereby ensuring accuracy retention. It incorporates data-aware gates to enhance the performance of the recommendation network by learning information from historical user-item interactions. Moreover, DNS-Rec employs a dynamic resource constraint strategy, stabilizing the search process and yielding more suitable architectural solutions. We demonstrate the effectiveness of our approach through rigorous experiments conducted on three benchmark datasets, which highlight the superiority of DNS-Rec in SRSs. Our findings set a new standard for future research in efficient and accurate recommendation systems, marking a significant step forward in this rapidly evolving field.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2402.00390
- https://arxiv.org/pdf/2402.00390
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391505507
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4391505507Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2402.00390Digital Object Identifier
- Title
-
DNS-Rec: Data-aware Neural Architecture Search for Recommender SystemsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-01Full publication date if available
- Authors
-
Sheng Zhang, Maolin Wang, Yao Zhao, Chenyi Zhuang, Jinjie Gu, Ruocheng Guo, Xiangyu Zhao, Zijian Zhang, Hongzhi YinList of authors in order
- Landing page
-
https://arxiv.org/abs/2402.00390Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2402.00390Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2402.00390Direct OA link when available
- Concepts
-
Term (time), Recommender system, Architecture, Computer science, Computer architecture, Information retrieval, Art, Visual arts, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Recommender | 32, 91 |
| abstract_inverted_index.consumption | 72 |
| abstract_inverted_index.demonstrate | 149 |
| abstract_inverted_index.development | 83 |
| abstract_inverted_index.efficiently | 6 |
| abstract_inverted_index.experiments | 157 |
| abstract_inverted_index.information | 10, 125 |
| abstract_inverted_index.maintaining | 74 |
| abstract_inverted_index.performance | 118 |
| abstract_inverted_index.recommender | 60 |
| abstract_inverted_index.significant | 188 |
| abstract_inverted_index.stabilizing | 138 |
| abstract_inverted_index.superiority | 166 |
| abstract_inverted_index.Architecture | 53, 88 |
| abstract_inverted_index.Furthermore, | 47 |
| abstract_inverted_index.contribution | 80 |
| abstract_inverted_index.incorporates | 112 |
| abstract_inverted_index.increasingly | 17 |
| abstract_inverted_index.inefficiency | 27 |
| abstract_inverted_index.specifically | 96 |
| abstract_inverted_index.architectural | 146 |
| abstract_inverted_index.architectures | 102 |
| abstract_inverted_index.computational | 23 |
| abstract_inverted_index.effectiveness | 151 |
| abstract_inverted_index.interactions. | 129 |
| abstract_inverted_index.proliferation, | 5 |
| abstract_inverted_index.recommendation | 121, 184 |
| abstract_inverted_index.attention-based | 104 |
| abstract_inverted_index.resource-constrained | 51 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |