This paper proposes new methods to enhance click-through rate (CTR)
prediction models using the Deep Interest Network (DIN) model, specifically
applied to the advertising system of Alibaba's Taobao platform. Unlike
traditional deep learning approaches, this research focuses on localized user
behavior activation for tailored ad targeting by leveraging extensive user
behavior data. Compared to traditional models, this method demonstrates
superior ability to handle diverse and dynamic user data, thereby improving the
efficiency of ad systems and increasing revenue.

本文提出了一种使用深度兴趣网络（DIN）模型来增强点击率（CTR）预测模型的新方法，特别应用于阿里巴巴淘宝平台的广告系统。与传统的深度学习方法不同，本研究侧重于利用广泛的用户行为数据进行定位用户行为激活以实现定制广告定位。与传统模型相比，该方法展现了处理多样化和动态用户数据的优越能力，从而提高广告系统的效率并增加收益。

在电子商务广告中使用深度兴趣网络模型预测点击率

Predict Click-Through Rates with Deep Interest Network Model in  E-commerce Advertising

Deep Interest Network (DIN) is a state-of-the-art model which uses attention
mechanism to capture user interests from historical behaviors. User interests
intuitively follow a hierarchical pattern such that users generally show
interests from a higher-level then to a lower-level abstraction. Modeling such
an interest hierarchy in an attention network can fundamentally improve the
representation of user behaviors. We, therefore, propose an improvement over
DIN to model arbitrary interest hierarchy: Deep Interest with Hierarchical
Attention Network (DHAN). In this model, a multi-dimensional hierarchical
structure is introduced on the first attention layer which attends to an
individual item, and the subsequent attention layers in the same dimension
attend to higher-level hierarchy built on top of the lower corresponding
layers. To enable modeling of multiple dimensional hierarchies, an expanding
mechanism is introduced to capture one to many hierarchies. This design enables
DHAN to attend different importance to different hierarchical abstractions thus
can fully capture user interests at different dimensions (e.g. category, price,
or brand).To validate our model, a simplified DHAN has applied to Click-Through
Rate (CTR) prediction and our experimental results on three public datasets
with two levels of the one-dimensional hierarchy only by category. It shows the
superiority of DHAN with significant AUC uplift from 12% to 21% over DIN. DHAN
is also compared with another state-of-the-art model Deep Interest Evolution
Network (DIEN), which models temporal interest. The simplified DHAN also gets
slight AUC uplift from 1.0% to 1.7% over DIEN. A potential future work can be a
combination of DHAN and DIEN to model both temporal and hierarchical interests.

DHAN 是 DIN 的改进版，引入多维层次结构，在第一个注意力层上引入分层机制，形成多个维度的分层关系，从而全面地捕捉用户在不同维度的兴趣爱好；该模型应用于 CTR 预测，并在三个公共数据集中得到显著的 AUC 改善，也与 DIEN 进行比较，该模型将是未来的研究方向，可以结合 DHAN 和 DIEN 以同时建模时间和分层结构的兴趣。