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.

通过引入 Farthest Greedy Path Sampling (FGPS) 和 Two-shot NAS (TS-NAS) 框架，本研究在复杂搜索空间中改进了权重共享神经结构搜索，以得到高性能的推荐模型，并在三个点击率预测基准中实现了优于手动设计和大多数基于 NAS 的模型的结果。