  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.
