PRECTR：一种集成个性化搜索相关性匹配与点击率预测的协同框架
PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction
AI总结：
提出PRECTR框架，通过条件概率融合机制统一搜索相关性匹配与CTR预测，引入两阶段训练和语义一致性正则化，并利用用户相关性偏好定制激励，实验证明其有效性。
摘要：
搜索推荐系统中的两个主要任务是搜索相关性匹配和点击率（CTR）预测——前者侧重于为用户查询寻找相关物品，而后者则预测哪个物品可能更符合用户兴趣。以往的研究通常开发两个模型分别预测CTR和搜索相关性，然后基于两个输出的融合对候选物品进行排序。然而，这种分而治之的范式造成了不同模型之间的不一致性。同时，搜索相关性模型主要关注客观文本匹配的程度，而忽略了不同用户之间的个性化差异，导致模型性能受限。为解决这些问题，我们提出了一个统一的个性化搜索相关性匹配与点击率预测融合模型（PRECTR）。具体而言，基于条件概率融合机制，PRECTR将CTR预测和搜索相关性匹配集成到一个框架中，以增强两个模块的交互和一致性。然而，直接优化CTR二分类损失可能会给融合模型的收敛带来挑战，并可能无限度地促进高CTR物品的曝光，而不管其搜索相关性如何。因此，我们进一步引入两阶段训练和语义一致性正则化，以加速模型收敛并抑制不相关物品的推荐。最后，考虑到不同用户可能具有不同的相关性偏好，我们通过分析过去用户对类似查询的偏好来评估当前用户的相关性偏好，并针对不同候选物品定制相应的激励。在我们生产数据集上的大量实验和在线A/B测试证明了我们提出的PRECTR方法的有效性和优越性。
英文摘要：
The two primary tasks in the search recommendation system are search relevance matching and click-through rate (CTR) prediction -- the former focuses on seeking relevant items for user queries whereas the latter forecasts which item may better match user interest. Prior research typically develops two models to predict the CTR and search relevance separately, then ranking candidate items based on the fusion of the two outputs. However, such a divide-and-conquer paradigm creates the inconsistency between different models. Meanwhile, the search relevance model mainly concentrates on the degree of objective text matching while neglecting personalized differences among different users, leading to restricted model performance. To tackle these issues, we propose a unified Personalized Search RElevance Matching and CTR Prediction Fusion Model(PRECTR). Specifically, based on the conditional probability fusion mechanism, PRECTR integrates the CTR prediction and search relevance matching into one framework to enhance the interaction and consistency of the two modules. However, directly optimizing CTR binary classification loss may bring challenges to the fusion model's convergence and indefinitely promote the exposure of items with high CTR, regardless of their search relevance. Hence, we further introduce two-stage training and semantic consistency regularization to accelerate the model's convergence and restrain the recommendation of irrelevant items. Finally, acknowledging that different users may have varied relevance preferences, we assessed current users' relevance preferences by analyzing past users' preferences for similar queries and tailored incentives for different candidate items accordingly. Extensive experimental results on our production dataset and online A/B testing demonstrate the effectiveness and superiority of our proposed PRECTR method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
