通过情境特征建模提升电商点击率预测效果
Deep Situation-Aware Interaction Network for Click-Through Rate Prediction
- 引入情境概念，融合行为类型、时间、位置等多维信息
- 在线测试中点击率提升2.70%，交易额增长2.16%
- 适合需要精准用户行为建模的推荐系统开发者
用户行为序列建模在电商平台点击率（CTR）预测中至关重要。除了交互商品外，用户行为还包含行为类型、时间、位置等丰富的互动信息，但现有方法尚未充分挖掘。本文提出情境与情境特征的概念，设计了深度情境感知交互网络（DSAIN）。DSAIN首先使用重参数化技巧降低原始行为序列噪声，再通过特征嵌入参数化和三向相关融合学习情境特征嵌入，最后通过异构情境聚合获取行为序列嵌入。在三个真实数据集上进行大量离线实验，结果表明该模型具有显著优势。更重要的是，在线上A/B测试中，DSAIN使点击率提升2.70%，每千次展示收益（CPM）提升2.62%，总商品交易额（GMV）提升2.16%。目前，DSAIN已部署于美团外卖平台，服务其核心流量。
原文摘要 · Abstract (English)
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.
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