Click-Through Rate (CTR) prediction, a cornerstone of modern recommender systems, has been dominated by discriminative models that react to past user behavior rather than proactively modeling user intent. Existing generative paradigms attempt to address this but suffer from critical limitations: Large Language Model (LLM) based methods create a semantic mismatch by forcing e-commerce signals into a linguistic space, while ID-based generation is constrained by item memorization and cold-start issues. To overcome these limitations, we propose a novel generative pre-training paradigm. Our model learns to predict the Next Interest Flow, a dense vector sequence representing a user's future intent, while simultaneously modeling its internal Interest Diversity and Interest Evolution Velocity to ensure the representation is both rich and coherent. However, this two-stage approach introduces a critical objective mismatch between the generative and discriminative stages. We resolve this via a bidirectional alignment strategy, which harmonizes the two stages through cross-stage weight initialization and a dynamic Semantic Alignment Module for fine-tuning. Additionally, we enhance the underlying discriminative model with a Temporal Sequential Pairwise (TSP) mechanism to better capture temporal causality. We present the All-domain Moveline Evolution Network (AMEN), a unified framework implementing our entire pipeline. Extensive offline experiments validate AMEN's superiority over strong baselines, and a large-scale online A/B test demonstrates its significant real-world impact, delivering substantial improvements in key business metrics.

翻译：点击率预测作为现代推荐系统的基石，长期被判别式模型主导，这类模型仅对用户历史行为作出反应，而非主动建模用户意图。现有生成式范式试图解决此问题，但存在关键局限：基于大语言模型的方法将电商信号强行映射至语言空间导致语义失配，而基于ID的生成则受限于物品记忆与冷启动问题。为突破这些限制，我们提出一种新颖的生成式预训练范式。该模型通过学习预测"下一代兴趣流"——表征用户未来意图的稠密向量序列，同时建模其内在的"兴趣多样性"与"兴趣演化速度"，确保表征兼具丰富性与连贯性。然而，这种两阶段方法在生成阶段与判别阶段间引入了关键的目标失配问题。我们通过双向对齐策略解决该问题：借助跨阶段权重初始化与动态语义对齐模块进行微调，实现两阶段协同。此外，我们为底层判别模型引入时序序列成对机制，以增强对时序因果关系的捕捉能力。我们提出全域移动轨迹演化网络——一个实现完整流程的统一框架。大量离线实验验证了AMEN相对于强基线的优越性，大规模在线A/B测试则证明其具有显著的实际应用价值，在关键业务指标上实现了实质性提升。