In e-commerce platforms, the relevant recommendation is a unique scenario
providing related items for a trigger item that users are interested in.
However, users' preferences for the similarity and diversity of recommendation
results are dynamic and vary under different conditions. Moreover, individual
item-level diversity is too coarse-grained since all recommended items are
related to the trigger item. Thus, the two main challenges are to learn
fine-grained representations of similarity and diversity and capture users'
dynamic preferences for them under different conditions. To address these
challenges, we propose a novel method called the Dynamic Preference-based and
Attribute-aware Network (DPAN) for predicting Click-Through Rate (CTR) in
relevant recommendations. Specifically, based on Attribute-aware Activation
Values Generation (AAVG), Bi-dimensional Compression-based Re-expression (BCR)
is designed to obtain similarity and diversity representations of user
interests and item information. Then Shallow and Deep Union-based Fusion (SDUF)
is proposed to capture users' dynamic preferences for the diverse degree of
recommendation results according to various conditions. DPAN has demonstrated
its effectiveness through extensive offline experiments and online A/B testing,
resulting in a significant 7.62% improvement in CTR. Currently, DPAN has been
successfully deployed on our e-commerce platform serving the primary traffic
for relevant recommendations. The code of DPAN has been made publicly
available.

电子商务平台中，我们提出了一种名为 DPAN 的新方法，通过学习细粒度的相似性和多样性表示并捕捉用户在不同条件下的动态偏好，来预测相关推荐中的点击率（CTR），经过离线实验和在线 A/B 测试的验证，CTR 提高了 7.62%。