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.

翻译：在电子商务平台中，相关推荐是一种独特场景，旨在为用户感兴趣的触发商品推荐相关物品。然而，用户对推荐结果相似性和多样性的偏好是动态变化的，且在不同条件下存在差异。此外，单个物品层面的多样性过于粗粒度，因为所有推荐物品均与触发商品相关。因此，主要挑战在于学习相似性和多样性的细粒度表示，并捕捉用户在不同条件下对它们的动态偏好。为应对这些挑战，我们提出了一种名为动态偏好和属性感知网络（DPAN）的新方法，用于预测相关推荐中的点击率（CTR）。具体而言，基于属性感知激活值生成（AAVG），设计了双向压缩重表示（BCR）来获取用户兴趣和物品信息的相似性与多样性表示。随后提出浅层与深层联合融合（SDUF），根据多种条件捕捉用户对推荐结果多样程度的动态偏好。通过广泛的离线实验和在线A/B测试，DPAN证明了其有效性，使CTR提升了7.62%。目前，DPAN已成功部署于我们的电子商务平台，为主要流量的相关推荐服务。DPAN的代码已公开提供。