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.

翻译：权重共享神经架构搜索（WS-NAS）为开发端到端深度推荐模型提供了一种高效机制。然而，在复杂搜索空间中，区分优劣架构（或路径）具有挑战性。这一挑战因超网络的有限覆盖率和子网络权重的共适应而加剧，从而限制了权重共享机制固有的探索与利用能力。为解决这些问题，我们提出最远贪婪路径采样（FGPS），这是一种平衡路径质量与多样性的新型路径采样策略。FGPS通过增强路径多样性促进更全面的超网络探索，同时强调路径质量以确保有效识别和利用有潜力的架构。通过将FGPS集成至两阶段NAS（TS-NAS）框架，我们推导出高性能架构。在三个点击率（CTR）预测基准上的评估表明，我们的方法持续取得更优结果，超越了手工设计模型及多数基于NAS的模型。