  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.
