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和搜索引擎之间的互动中提取隐含的相关性反馈信息对于提高搜索结果的排序非常重要。 使用神经网络模拟用户的点击行为已经成为构建点击模型的有效方法之一。 在本文中, 我们使用变换器作为地貌提取的主网, 添加过滤器层, 并提出一个新的过滤器强化变换器点击模型( FE- TCM ) 用于网络搜索。 首先, 为了减少噪音对用户行为数据的影响, 我们使用可学过滤器来过滤日志噪音。 其次, 在测试假设之后, 我们分别模拟吸引估计和测试预测器, 以输出吸引力评分和测试概率。 一种新型变换模型用来学习不同特性之间的更深的表达方式。 最后, 我们应用组合功能将吸引力评分和测试概率纳入点击预测。 从两个真实世界会话数据集的实验中, 证明FE- TCM 超越了当前点击预测模型。