# DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems

arXiv: https://arxiv.org/abs/2402.00390 · HTML: https://mlsanity.com/paper/2402.00390/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Deep Reinforcement Learning for Page-wise Recommendations](https://mlsanity.com/paper/1805.02343/) (2018)
- [Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning](https://mlsanity.com/paper/1802.06501/) (2018)
- [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://mlsanity.com/paper/2302.01115/) (2023)
- [DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems](https://mlsanity.com/paper/1909.03602/) (2019)
- [CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space](https://mlsanity.com/paper/2204.11067/) (2022)
- [AutoLoss: Automated Loss Function Search in Recommendations](https://mlsanity.com/paper/2106.06713/) (2021)
- [AutoField: Automating Feature Selection in Deep Recommender Systems](https://mlsanity.com/paper/2204.09078/) (2022)
- [AutoMLP: Automated MLP for Sequential Recommendations](https://mlsanity.com/paper/2303.06337/) (2023)
- [AutoDenoise: Automatic Data Instance Denoising for Recommendations](https://mlsanity.com/paper/2303.06611/) (2023)
- [User Retention-oriented Recommendation with Decision Transformer](https://mlsanity.com/paper/2303.06347/) (2023)
- [AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation](https://mlsanity.com/paper/2205.08776/) (2022)

## Built on by (most cited first, 4 of 4)

- [Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback](https://mlsanity.com/paper/2602.12612/) (2026)
- [DANCE: Resource-Efficient Neural Architecture Search with Data-Aware and Continuous Adaptation](https://mlsanity.com/paper/2507.04671/) (2025)
- [Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks](https://mlsanity.com/paper/2506.03391/) (2025)
- [Renormalization Group Guided Tensor Network Structure Search](https://mlsanity.com/paper/2512.24663/) (2025)

## Abstract (arXiv)

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.