AI 中文总结
针对触发式推荐中点击率预测的需求，提出CRRN模型，通过触发-目标交互层、级联兴趣融合模块和类别辅助成对损失优化，在工业与公开数据集及在线A/B测试中均优于现有方法。
AI 中文摘要
电子商务已成为人们日常消费和购物兴趣的重要平台。存在一种新的推荐场景——触发式推荐（Trigger-Introduced Recommendation, TIR），其中用户点击感兴趣的商品，该商品被定义为触发项，包含其即时兴趣，并在后续页面中呈现相关目标商品。与传统搜索和推荐场景不同，触发项包含相对较强的即时兴趣，与搜索词相比更模糊且隐含。现有方法依赖大量标注数据，缺乏对触发项相关性的探索，这会影响用户的沉浸式体验。为缓解该问题，我们提出级联相关性驱动推荐网络（Cascading Relevance-driven Recommendation Network, CRRN），以强调触发项与目标项之间的交互和相关性，该网络包含三个关键组件：1）触发项-目标项交互层，基于个性化门控机制提取触发项与目标项的交互特征；2）级联兴趣融合模块，通过级联注意力模块显式估计用户的触发意图，并自适应融合即时兴趣与个性化兴趣；3）类别辅助成对损失，在触发项与目标项的类别关联指导下增强触发项相关性。大量实验结果表明，CRRN在工业数据集和公开数据集上均优于近期的最先进方法；在线A/B测试进一步验证了我们方法的有效性，代码可在指定网址获取。
英文摘要
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.
