PRECTR-V2:一种结合跨用户偏好挖掘、曝光偏差校正和LLM蒸馏编码器优化的统一相关性-CTR框架
PRECTR-V2:Unified Relevance-CTR Framework with Cross-User Preference Mining, Exposure Bias Correction, and LLM-Distilled Encoder Optimization
AI总结：
PRECTR-V2通过跨用户偏好挖掘、曝光偏差校正和LLM蒸馏编码器优化，解决搜索系统中相关性匹配与CTR预测的一致性问题。
摘要：
在搜索系统中，有效协调搜索相关性匹配和点击率（CTR）预测这两个核心目标对于发现用户兴趣和提高平台收入至关重要。在我们之前的工作PRECTR中，我们提出了一种统一框架来整合这两个子任务，从而消除它们的一致性问题并实现相互受益。然而，我们的先前工作仍然面临三个主要挑战。首先，低活跃用户和新用户具有有限的搜索行为数据，这使得实现有效的个性化相关性偏好建模变得困难。其次，排序模型的训练数据主要来自高相关性曝光，导致与粗排序中更广泛候选空间的分布不匹配，从而产生泛化偏差。第三，由于延迟限制，原始模型采用Emb+MLP架构，配合冻结的BERT编码器，这阻止了联合优化，并导致表示学习与CTR微调之间的不一致。为了解决这些问题，我们进一步加强了我们的方法并提出PRECTR-V2。具体来说，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的稀疏行为问题，这有助于为冷启动场景实现有效的个性化相关性建模。随后，我们通过嵌入噪声注入和相关性标签重建构建硬负样本，并通过成对损失优化其相对于正样本的相对排序，从而校正曝光偏差。最后，我们通过从LLM和SFT上文本相关性分类任务的知识蒸馏预训练了一个轻量级的Transformer编码器。这个编码器取代了冻结的BERT模块，使模型更好地适应CTR微调，并超越了传统的Emb+MLP范式。
英文摘要：
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.
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