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[2602.13971] DAIAN: Deep Adaptive Intent-Aware Network for CTR Prediction in Trigger-Induced Recommendation
Summary
The paper presents DAIAN, a Deep Adaptive Intent-Aware Network designed to enhance Click-Through Rate (CTR) prediction in Trigger-Induced Recommendation systems by addressing intent myopia and improving user intent representation.
Why It Matters
As e-commerce continues to grow, effective recommendation systems are crucial for personalizing user experiences. DAIAN's approach to overcoming limitations in existing models could lead to more accurate and relevant recommendations, ultimately improving user engagement and satisfaction.
Key Takeaways
- DAIAN addresses intent myopia by dynamically adapting to user preferences.
- The model enhances user intent representation through historical behavior analysis.
- It utilizes a hybrid enhancer to improve item similarity based on ID and semantic information.
- Experimental results show DAIAN's effectiveness on both public and industrial datasets.
- This research contributes to the advancement of personalized recommendation systems in e-commerce.
Computer Science > Information Retrieval arXiv:2602.13971 (cs) [Submitted on 15 Feb 2026] Title:DAIAN: Deep Adaptive Intent-Aware Network for CTR Prediction in Trigger-Induced Recommendation Authors:Zhihao Lv, Longtao Zhang, Ailong He, Shuzhi Cao, Shuguang Han, Jufeng Chen View a PDF of the paper titled DAIAN: Deep Adaptive Intent-Aware Network for CTR Prediction in Trigger-Induced Recommendation, by Zhihao Lv and 5 other authors View PDF HTML (experimental) Abstract:Recommendation systems are essential for personalizing e-commerce shopping experiences. Among these, Trigger-Induced Recommendation (TIR) has emerged as a key scenario, which utilizes a trigger item (explicitly represents a user's instantaneous interest), enabling precise, real-time recommendations. Although several trigger-based techniques have been proposed, most of them struggle to address the intent myopia issue, that is, a recommendation system overemphasizes the role of trigger items and narrowly focuses on suggesting commodities that are highly relevant to trigger items. Meanwhile, existing methods rely on collaborative behavior patterns between trigger and recommended items to identify the user's preferences, yet the sparsity of ID-based interaction restricts their effectiveness. To this end, we propose the Deep Adaptive Intent-Aware Network (DAIAN) that dynamically adapts to users' intent preferences. In general, we first extract the users' personalized intent representations by analyzing the correlat...