In search systems, effectively coordinating the two core objectives of search relevance matching and click-through rate (CTR) prediction is crucial for discovering users' interests and enhancing platform revenue. In our prior work PRECTR, we proposed a unified framework to integrate these two subtasks,thereby eliminating their inconsistency and leading to mutual benefit.However, our previous work still faces three main challenges. First, low-active users and new users have limited search behavioral data, making it difficult to achieve effective personalized relevance preference modeling. Second, training data for ranking models predominantly come from high-relevance exposures, creating a distribution mismatch with the broader candidate space in coarse-ranking, leading to generalization bias. Third, due to the latency constraint, the original model employs an Emb+MLP architecture with a frozen BERT encoder, which prevents joint optimization and creates misalignment between representation learning and CTR fine-tuning. To solve these issues, we further reinforce our method and propose PRECTR-V2. Specifically, we mitigate the low-activity users' sparse behavior problem by mining global relevance preferences under the specific query, which facilitates effective personalized relevance modeling for cold-start scenarios. Subsequently, we construct hard negative samples through embedding noise injection and relevance label reconstruction, and optimize their relative ranking against positive samples via pairwise loss, thereby correcting exposure bias. Finally, we pretrain a lightweight transformer-based encoder via knowledge distillation from LLM and SFT on the text relevance classification task. This encoder replaces the frozen BERT module, enabling better adaptation to CTR fine-tuning and advancing beyond the traditional Emb+MLP paradigm.

翻译：在搜索系统中，有效协调搜索相关性匹配与点击率预测这两个核心目标，对于发掘用户兴趣和提升平台收益至关重要。在我们先前的工作PRECTR中，我们提出了一个统一框架来整合这两个子任务，从而消除其不一致性并实现相互促进。然而，我们之前的工作仍面临三个主要挑战。首先，低活跃用户和新用户的搜索行为数据有限，难以实现有效的个性化相关性偏好建模。其次，排序模型的训练数据主要来自高相关性曝光，与粗排阶段更广泛的候选空间存在分布不匹配，导致泛化偏差。第三，由于延迟约束，原始模型采用Emb+MLP架构并冻结BERT编码器，这阻碍了联合优化，并导致表征学习与CTR微调之间的错位。为解决这些问题，我们进一步强化了方法并提出了PRECTR-V2。具体而言，我们通过在特定查询下挖掘全局相关性偏好来缓解低活跃用户行为稀疏的问题，从而促进冷启动场景下的有效个性化相关性建模。随后，我们通过嵌入噪声注入和相关性标签重构构建困难负样本，并通过成对损失优化其相对于正样本的排序，从而校正曝光偏差。最后，我们通过从LLM进行知识蒸馏并在文本相关性分类任务上进行SFT，预训练了一个轻量化的基于Transformer的编码器。该编码器替代了冻结的BERT模块，能够更好地适应CTR微调，并超越了传统的Emb+MLP范式。