CAPTS: Channel-Aware, Preference-Aligned Trigger Selection for Multi-Channel Item-to-Item Retrieval

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Large-scale industrial recommender systems 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 item-to-item (I2I) retrieval across multiple channels. In production, triggers are often selected using rule-based policies or learned scorers and tuned channel by channel. However, these practices face two challenges: biased value attribution, which values triggers by on-trigger feedback rather than downstream retrieval utility, and uncoordinated routing, where channels independently select triggers under a shared quota, increasing cross-channel overlap. To address these challenges, we propose Channel-Aware, Preference-Aligned Trigger Selection (CAPTS), a framework that treats multi-channel trigger selection as a learnable routing problem. CAPTS introduces a Value Attribution Module (VAM) that credits each trigger with subsequent engagement from items retrieved through each I2I channel, and a Channel-Adaptive Trigger Routing (CATR) module that coordinates trigger-to-channel assignment. 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 +0.713% total app time spent and +0.586% average app time spent per device online.