In Click-through rate (CTR) prediction models, a user's interest is usually
represented as a fixed-length vector based on her history behaviors. Recently,
several methods are proposed to learn an attentive weight for each user
behavior and conduct weighted sum pooling. However, these methods only manually
select several fields from the target item side as the query to interact with
the behaviors, neglecting the other target item fields, as well as user and
context fields. Directly including all these fields in the attention may
introduce noise and deteriorate the performance. In this paper, we propose a
novel model named AutoAttention, which includes all item/user/context side
fields as the query, and assigns a learnable weight for each field pair between
behavior fields and query fields. Pruning on these field pairs via these
learnable weights lead to automatic field pair selection, so as to identify and
remove noisy field pairs. Though including more fields, the computation cost of
AutoAttention is still low due to using a simple attention function and field
pair selection. Extensive experiments on the public dataset and Tencent's
production dataset demonstrate the effectiveness of the proposed approach.

提出了一种名为 AutoAttention 的模型，该模型使用可学习的权重对所有 item/user/context 字段进行查询，以及在行为字段和查询字段之间为每个字段对分配一个可学习的权重对这些字段对进行修剪，从而实现自动字段对选择，以去除噪声字段。