CAPTS: Channel-Aware, Preference-Aligned Trigger Selection for Multi-Channel Item-to-Item Retrieval
Xiaoyou Zhou, Yuqi Liu, Zhao Liu, Xiao Lv, Bo Chen, Ruiming Tang, Guorui Zhou

TL;DR
This paper introduces CAPTS, a novel framework for multi-channel trigger selection in recommender systems that improves retrieval effectiveness by addressing bias and uncoordinated routing through learnable modules.
Contribution
CAPTS is a unified, learnable trigger selection framework that enhances multi-channel retrieval by crediting triggers with downstream engagement and coordinating trigger-to-channel assignment.
Findings
Improves multi-channel recall offline
Achieves +0.351% lift in online user engagement
Demonstrates effectiveness in large-scale online platform
Abstract
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…
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Taxonomy
TopicsRecommender Systems and Techniques · Information Retrieval and Search Behavior · Multimodal Machine Learning Applications
