摘要： 在电子商务平台中，用户的点击数据急剧增长。在推荐系统中通过对电商用户的长期行为序列建模，对于捕获用户的偏好至关重要。目前普遍使用两阶段点击率（Click-through Rate , CTR）预测模型来预测长序列用户的点击率，即第一阶段通过近似检索从海量历史行为中筛选与目标项目相关的子序列，第二阶段对子序列进行精细兴趣建模。但是，在两阶段模型中存在着第二阶段搜索过程中存在着较少关注用户行为趋势性特征的问题；存在着跨阶段语义错配问题，导致第二阶段子序列未能完整传递用户真实兴趣结构。为此，提出一种可以感知趋势的概率注意力结构。该模型提出趋势感知特征建模，捕捉用户行为中的时序趋势。且结合概率注意力机制，统一跨阶段的兴趣表征，显著提升了长序列电商用户点击率预测的预测精度。为了验证模型的有效性，在两个真实的电商数据集上进行了实验。与最先进的基线模型相比，该模型在AUC和Logloss两个指标上最高提升了1.14%和4.2%。说明该模型不仅能识别用户行为中的趋势特征与动态偏好结构，更验证了跨阶段语义一致性的优化价值。

Abstract: In e-commerce platforms, the volume of user click data is experiencing a rapid increase. Accurately modeling long-term behavior sequences of e-commerce users is crucial for capturing their preferences in recommendation systems. Currently, two-stage Click-through Rate (CTR) prediction models are widely used to forecast the CTR of users with long behavioral sequences. Specifically, the first stage employs approximate retrieval to filter subsequences related to the target item from massive historical behaviors, while the second stage performs fine-grained interest modeling on these subsequences. However, the two-stage model has two key issues: first, the second-stage process pays insufficient attention to the trend characteristics of user behavior; second, there exists a cross-stage semantic mismatch, which causes the second-stage subsequences to fail in fully conveying the users’ true interest structure. To address these issues, we propose a trend-aware probabilistic attention architecture. This model captures temporal trends in user behaviors and unifies interest representations across stages, significantly improving CTR prediction accuracy for long sequences. Experiments on two real-world e-commerce datasets show that our model outperforms state-of-the-art baselines, achieving up to 1.14% improvement in AUC and 4.2% in Logloss. This demonstrates that the model not only can identify the trend characteristics and dynamic preference structures in user behavior, but also verifies the optimization value of cross-stage semantic consistency.