# Farthest Greedy Path Sampling for Two-shot Recommender Search

arXiv: https://arxiv.org/abs/2310.20705 · HTML: https://mlsanity.com/paper/2310.20705/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 44 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Single Path One-Shot Neural Architecture Search with Uniform Sampling](https://mlsanity.com/paper/1904.00420/) (2019)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://mlsanity.com/paper/1906.00091/) (2019)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://mlsanity.com/paper/1708.04617/) (2017)
- [Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap](https://mlsanity.com/paper/2008.01475/) (2020)
- [MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask](https://mlsanity.com/paper/2102.07619/) (2021)
- [Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction](https://mlsanity.com/paper/2007.06434/) (2020)
- [GreedyNASv2: Greedier Search with a Greedy Path Filter](https://mlsanity.com/paper/2111.12609/) (2021)
- [NASRec: Weight Sharing Neural Architecture Search for Recommender Systems](https://mlsanity.com/paper/2207.07187/) (2022)
- [Differentiable NAS Framework and Application to Ads CTR Prediction](https://mlsanity.com/paper/2110.14812/) (2021)

## Abstract (arXiv)

Weight-sharing Neural Architecture Search (WS-NAS) provides an efficient mechanism for developing end-to-end deep recommender models. However, in complex search spaces, distinguishing between superior and inferior architectures (or paths) is challenging. This challenge is compounded by the limited coverage of the supernet and the co-adaptation of subnet weights, which restricts the exploration and exploitation capabilities inherent to weight-sharing mechanisms. To address these challenges, we introduce Farthest Greedy Path Sampling (FGPS), a new path sampling strategy that balances path quality and diversity. FGPS enhances path diversity to facilitate more comprehensive supernet exploration, while emphasizing path quality to ensure the effective identification and utilization of promising architectures. By incorporating FGPS into a Two-shot NAS (TS-NAS) framework, we derive high-performance architectures. Evaluations on three Click-Through Rate (CTR) prediction benchmarks demonstrate that our approach consistently achieves superior results, outperforming both manually designed and most NAS-based models.