With the increasing complexity and scale of click-through rate (CTR) prediction tasks in online advertising and recommendation systems, accurately estimating the importance of features has become a critical aspect of developing effective models. In this paper, we propose an attention-based approach that leverages max and mean pooling operations, along with a bit-wise attention mechanism, to enhance feature importance estimation in CTR prediction. Traditionally, pooling operations such as max and mean pooling have been widely used to extract relevant information from features. However, these operations can lead to information loss and hinder the accurate determination of feature importance. To address this challenge, we propose a novel attention architecture that utilizes a bit-based attention structure that emphasizes the relationships between all bits in features, together with maximum and mean pooling. By considering the fine-grained interactions at the bit level, our method aims to capture intricate patterns and dependencies that might be overlooked by traditional pooling operations. To examine the effectiveness of the proposed method, experiments have been conducted on three public datasets. The experiments demonstrated that the proposed method significantly improves the performance of the base models to achieve state-of-the-art results.

翻译：随着在线广告和推荐系统中点击率（CTR）预测任务的复杂性和规模不断增加，准确估计特征重要性成为开发高效模型的关键环节。本文提出一种基于注意力机制的方法，结合最大池化和均值池化操作以及逐位注意力机制，以增强CTR预测中的特征重要性估计。传统上，最大池化和均值池化等操作被广泛用于从特征中提取相关信息，然而这些操作可能导致信息丢失，从而妨碍对特征重要性的准确判定。为应对这一挑战，我们提出一种新颖的注意力架构，该架构利用基于比特的注意力结构（强调特征中所有比特之间的关联性）并结合最大池化与均值池化。通过考虑比特级别的细粒度交互，我们的方法旨在捕捉传统池化操作可能忽略的复杂模式与依赖关系。为验证所提方法的有效性，我们在三个公开数据集上进行了实验。结果表明，该方法显著提升了基础模型的性能，达到了当前最优结果。