Cascading Relevance-driven Recommendation Network for CTR Prediction in Trigger-Introduced Recommendation

• URL: http://arxiv.org/abs/2608.22973v1
• Date: Mon, 24 Aug 2026 08:33:14 GMT
• Title: Cascading Relevance-driven Recommendation Network for CTR Prediction in Trigger-Introduced Recommendation
• Abstract summary: E-commerce has emerged as crucial platforms for people's daily consumption and shopping interests.<n>There is a new recommendation scenario, Trigger-Introduced Recommendation (TIR), where users click interested product.<n>We propose the Cascading Relevance-driven Recommendation Network (CRRN) to emphasize the interaction and relevance between trigger and target.
• Score: 7.455067639822993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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