User behavior sequence modeling plays a significant role in Click-Through Rate (CTR) prediction on e-commerce platforms. Except for the interacted items, user behaviors contain rich interaction information, such as the behavior type, time, location, etc. However, so far, the information related to user behaviors has not yet been fully exploited. In the paper, we propose the concept of a situation and situational features for distinguishing interaction behaviors and then design a CTR model named Deep Situation-Aware Interaction Network (DSAIN). DSAIN first adopts the reparameterization trick to reduce noise in the original user behavior sequences. Then it learns the embeddings of situational features by feature embedding parameterization and tri-directional correlation fusion. Finally, it obtains the embedding of behavior sequence via heterogeneous situation aggregation. We conduct extensive offline experiments on three real-world datasets. Experimental results demonstrate the superiority of the proposed DSAIN model. More importantly, DSAIN has increased the CTR by 2.70\%, the CPM by 2.62\%, and the GMV by 2.16\% in the online A/B test. Now, DSAIN has been deployed on the Meituan food delivery platform and serves the main traffic of the Meituan takeout app.

翻译：用户行为序列建模在电商平台的点击率（CTR）预测中起着重要作用。除了已交互的商品外，用户行为还包含丰富的交互信息，如行为类型、时间、地点等。然而，迄今为止，与用户行为相关的信息尚未得到充分利用。本文提出情境及情境特征的概念以区分交互行为，并设计了一种名为“深度情景感知交互网络”（DSAIN）的CTR模型。DSAIN首先采用重参数化技巧降低原始用户行为序列中的噪声；随后通过特征嵌入参数化与三向相关性融合学习情境特征的嵌入表示；最后通过异构情境聚合获得行为序列的嵌入表示。我们在三个真实数据集上进行了广泛的离线实验，实验结果证明了所提DSAIN模型的优越性。更重要的是，在线A/B测试中，DSAIN使CTR提升2.70%、CPM提升2.62%、GMV提升2.16%。目前，DSAIN已在美团外卖平台部署，并服务于美团外卖应用的主要流量。