Cascading Relevance-driven Recommendation Network for CTR Prediction in Trigger-Introduced Recommendation
2026-08-24 • Information Retrieval
Information Retrieval
AI summaryⓘ
The authors study a new kind of product recommendation called Trigger-Introduced Recommendation (TIR), where a user first clicks on a 'trigger' product that shows their immediate interest before seeing related target items. They point out that this trigger is different from typical search terms because it reflects a more vague and instant interest. To improve recommendations, the authors design a model called CRRN that better links triggers and target items by focusing on their interaction, user intention, and category relationships. Their experiments show CRRN works better than previous methods, and they verified its effectiveness with live tests.
Trigger-Introduced RecommendationE-commerce recommendationUser interest modelingPersonalized gatingAttention mechanismsPairwise lossCategory associationA/B testingInteraction features
Authors
Kaixuan Chen, Wenwen Wang, Xing Fang, Yang Huang, Jing Wang
Abstract
E-commerce has emerged as crucial platforms for people's daily consumption and shopping interests. There is a new recommendation scenario, Trigger-Introduced Recommendation (TIR), where users click interested product, which is defined as the trigger item, containing their instant interest, and in the undertaking page following the relevant target items. Distinguished from traditional search and recommendation scenarios, trigger contains relatively strong instant interest, which is more vague and implicit compared to search terms. Relying on large amounts of labeled data, existing methods lack the exploration of trigger relevance, which affects users' immersive experience. To alleviate this problem, we propose the Cascading Relevance-driven Recommendation Network (CRRN) to emphasize the interaction and relevance between trigger and target, comprising three essential components: 1) the Trigger-Target Interaction layer extracts interaction features of trigger and target based on personalized gating. 2) Cascading Interest Fusion module explicitly estimates users' trigger intention and fuses instant and personalized interests adaptively with cascading attention blocks. 3) Category-assisted Pairwise Loss enhances trigger relevance with the guidance of category association between trigger and target. Extensive experiment results show that CRRN outperforms recent state-of-the-art methods on both industrial and public datasets. Online A/B tests further validate the effectiveness of our method. Our code is available at https://github.com/a-little-cabbage/CRRN.