Constructing click models and extracting implicit relevance feedback information from the interaction between users and search engines are very important to improve the ranking of search results. Using neural network to model users' click behaviors has become one of the effective methods to construct click models. In this paper, We use Transformer as the backbone network of feature extraction, add filter layer innovatively, and propose a new Filter-Enhanced Transformer Click Model (FE-TCM) for web search. Firstly, in order to reduce the influence of noise on user behavior data, we use the learnable filters to filter log noise. Secondly, following the examination hypothesis, we model the attraction estimator and examination predictor respectively to output the attractiveness scores and examination probabilities. A novel transformer model is used to learn the deeper representation among different features. Finally, we apply the combination functions to integrate attractiveness scores and examination probabilities into the click prediction. From our experiments on two real-world session datasets, it is proved that FE-TCM outperforms the existing click models for the click prediction.

翻译：构建点击模型并从用户与搜索引擎的交互中提取隐式相关性反馈信息，对于提升搜索结果排序至关重要。利用神经网络建模用户点击行为已成为构建点击模型的有效方法之一。本文以Transformer作为特征提取的主干网络，创新性地引入滤波器层，提出了一种新的滤波器增强Transformer点击模型（FE-TCM）。首先，为降低噪声对用户行为数据的影响，我们采用可学习滤波器对日志噪声进行过滤。其次，遵循检验假设，分别建模吸引力估计器和检验预测器，输出吸引力分数与检验概率。通过新型Transformer模型学习不同特征间的深层表示。最后，采用组合函数将吸引力分数与检验概率整合为点击预测。在两个真实会话数据集上的实验证明，FE-TCM在点击预测任务上优于现有点击模型。