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 correlation between a user's click and the trigger item, and accordingly retrieve the user's related historical behaviors to mine the user's diverse intent. Besides, sparse collaborative behaviors constrain the performance in capturing items associated with user intent. Hence, we reinforce similarity by leveraging a hybrid enhancer with ID and semantic information, followed by adaptive selection based on varying intents. Experimental results on public datasets and our industrial e-commerce datasets demonstrate the effectiveness of DAIAN.

翻译：推荐系统对于个性化电子商务购物体验至关重要。其中，触发诱导推荐（TIR）已成为一个关键场景，它利用触发项（明确表示用户的瞬时兴趣）来实现精确的实时推荐。尽管已有多种基于触发项的技术被提出，但大多数方法难以解决意图短视问题，即推荐系统过度强调触发项的作用，并狭隘地聚焦于推荐与触发项高度相关的商品。同时，现有方法依赖于触发项与推荐项之间的协同行为模式来识别用户偏好，然而基于ID的交互稀疏性限制了其有效性。为此，我们提出了深度自适应意图感知网络（DAIAN），该网络能动态适应用户的意图偏好。总体而言，我们首先通过分析用户点击与触发项之间的相关性来提取用户的个性化意图表示，并据此检索用户相关的历史行为以挖掘用户的多样化意图。此外，稀疏的协同行为限制了模型在捕捉与用户意图相关物品方面的性能。因此，我们利用结合ID与语义信息的混合增强器来强化相似性，随后根据不同意图进行自适应选择。在公共数据集和我们工业级电子商务数据集上的实验结果验证了DAIAN的有效性。