Large-scale industrial recommender systems commonly adopt multi-channel retrieval for candidate generation, combining direct user-to-item (U2I) retrieval with two-hop user-to-item-to-item (U2I2I) pipelines. In U2I2I, the system selects a small set of historical interactions as triggers to seed downstream item-to-item (I2I) retrieval across multiple channels. In production, triggers are often selected using rule-based policies or learned scorers and tuned in a channel-by-channel manner. However, these practices face two persistent challenges: biased value attribution that values triggers by on-trigger feedback rather than their downstream utility as retrieval seeds, and uncoordinated multi-channel routing where channels select triggers independently under a shared quota, increasing cross-channel overlap. To address these challenges, we propose Channel-Aware, Preference-Aligned Trigger Selection (CAPTS), a unified and flexible framework that treats multi-channel trigger selection as a learnable routing problem. CAPTS introduces a Value Attribution Module (VAM) that provides look-ahead supervision by crediting each trigger with the subsequent engagement generated by items retrieved from it on each I2I channel, and a Channel-Adaptive Trigger Routing (CATR) module that coordinates trigger-to-channel assignment to maximize the overall value of multi-channel retrieval. Extensive offline experiments and large-scale online A/B tests on Kwai, Kuaishou's international short-video platform, show that CAPTS consistently improves multi-channel recall offline and delivers a +0.351% lift in average time spent per device online.

翻译：大规模工业推荐系统通常采用多通道检索进行候选生成，将直接用户到商品（U2I）检索与两跳用户到商品到商品（U2I2I）流水线相结合。在U2I2I中，系统选择少量历史交互作为触发项，为下游跨多个通道的商品到商品（I2I）检索提供种子。在实际生产中，触发项通常通过基于规则的策略或学习型评分器进行选择，并以逐通道方式进行调优。然而，这些实践面临两个持续存在的挑战：一是存在偏置的价值归因，即根据触发项本身的反馈而非其作为检索种子的下游效用评估触发项价值；二是多通道路由缺乏协调，各通道在共享配额下独立选择触发项，导致跨通道重叠增加。为应对这些挑战，我们提出通道感知偏好对齐触发选择（CAPTS），这是一个统一且灵活的框架，将多通道触发选择视为可学习的路由问题。CAPTS引入了价值归因模块（VAM），通过将每个I2I通道上由触发项检索所得商品产生的后续参与度归功于对应触发项，从而提供前瞻性监督；以及通道自适应触发路由（CATR）模块，该模块协调触发项到通道的分配，以最大化多通道检索的整体价值。在快手国际短视频平台Kwai上进行的大规模离线实验和在线A/B测试表明，CAPTS在离线环境下持续提升多通道召回率，并在在线环境中实现日均设备使用时长提升+0.351%。