SCASRec: 一种自校正和自动停止的生成路线推荐模型
SCASRec: A Self-Correcting and Auto-Stopping Model for Generative Route List Recommendation
AI总结：
SCASRec通过整合排序与冗余消除，解决传统多阶段推荐中目标不一致、冗余处理不灵活和阶段分离导致的性能问题，实现端到端优化。
摘要：
路线推荐系统通常采用细排和再排的多阶段流程生成高质量的有序推荐。然而，这一范式面临三个关键限制。首先，离线训练目标与在线指标之间存在不一致，离线收益不一定转化为在线改进。实际性能必须通过A/B测试验证，可能影响用户体验。其次，冗余消除依赖于刚性的手工规则，无法适应用户意图的高变异性及现实场景的非结构化复杂性。第三，细排与再排阶段的严格分离导致性能低下。由于每个模块独立优化，细排阶段无法感知再排器的目标（如多样性），从而无法实现联合优化的全局最优。为克服这些交织挑战，我们提出SCASRec（自校正和自动停止推荐），一种统一的生成框架，将排序与冗余消除整合到单个端到端流程中。SCASRec引入逐步纠正奖励（SCR）通过聚焦困难样本指导列表级细化，并采用可学习的End-of-Recommendation（EOR）标记，在预期无进一步改进时自适应终止生成。在两个大规模开源路线推荐数据集上的实验表明，SCASRec在离线和在线设置中均达到SOTA。SCASRec已在真实世界导航应用中完全部署，证明其有效性。
英文摘要：
Route recommendation systems commonly adopt a multi-stage pipeline involving fine-ranking and re-ranking to produce high-quality ordered recommendations. However, this paradigm faces three critical limitations. First, there is a misalignment between offline training objectives and online metrics. Offline gains do not necessarily translate to online improvements. Actual performance must be validated through A/B testing, which may potentially compromise the user experience. Second, redundancy elimination relies on rigid, handcrafted rules that lack adaptability to the high variance in user intent and the unstructured complexity of real-world scenarios. Third, the strict separation between fine-ranking and re-ranking stages leads to sub-optimal performance. Since each module is optimized in isolation, the fine-ranking stage remains oblivious to the list-level objectives (e.g., diversity) targeted by the re-ranker, thereby preventing the system from achieving a jointly optimized global optimum. To overcome these intertwined challenges, we propose SCASRec (Self-Correcting and Auto-Stopping Recommendation), a unified generative framework that integrates ranking and redundancy elimination into a single end-to-end process. SCASRec introduces a stepwise corrective reward (SCR) to guide list-wise refinement by focusing on hard samples, and employs a learnable End-of-Recommendation (EOR) token to terminate generation adaptively when no further improvement is expected. Experiments on two large-scale, open-sourced route recommendation datasets demonstrate that SCASRec establishes an SOTA in offline and online settings. SCASRec has been fully deployed in a real-world navigation app, demonstrating its effectiveness.
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。
