面向两阶段推荐系统搜索的最远贪心路径采样
Farthest Greedy Path Sampling for Two-shot Recommender Search
AI总结：
本文提出最远贪心路径采样FGPS，通过平衡路径质量与多样性改进权重共享NAS，并结合两阶段NAS框架在三个CTR预测基准上取得优于人工设计和多数NAS模型的性能。
摘要：
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。这一挑战因超网覆盖范围有限以及子网权重的协同适应而加剧，限制了权重共享机制固有的探索与利用能力。为应对这些挑战，我们提出最远贪心路径采样（FGPS），这是一种新的路径采样策略，能够平衡路径质量与多样性。FGPS 增强路径多样性以促进更全面的超网探索，同时强调路径质量以确保有效识别和利用有前景的架构。通过将 FGPS 纳入两阶段 NAS（TS-NAS）框架，我们获得了高性能架构。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优越结果，优于人工设计模型和大多数基于 NAS 的模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
