为点击和转化信号分别设计编码器，提升离站转化率预测效果
DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction
- 将用户行为分为点击与转化两路，用不同架构的Transformer分别建模
- 相比最强基线，归一化熵降低0.38%，离站转化率预测更准
- 适合需要高精度转化预估的推荐系统场景
离站转化率（OCVR）预测是计算推荐系统中的关键排序问题。该任务面临建模挑战：点击信号丰富且时间跨度短，而转化信号稀疏、延迟长且常无法归因。尽管存在这些统计差异，模型仍需在严格的服务延迟约束下运行。现有预训练方法对两类数据流使用单一统一编码器。我们提出DUET（双用户嵌入Transformer），显式将用户行为划分为点击与转化两个领域一致的数据流，并为每一流量定制专用Transformer编码器：针对密集点击流采用多层自注意力，针对稀疏转化流采用交错的交叉与自注意力结构。生成的互补嵌入由下游排序器联合使用，且不超出服务延迟预算。评估显示，相较于最强基线，归一化熵降低最多0.38%；A/B测试也验证了在OCVR预测准确性上的持续提升。
原文摘要 · Abstract (English)
Offsite conversion rate (OCVR) prediction is an important ranking problem in computational recommendation systems. This task presents a modeling challenge: click signals are abundant and exhibit short temporal horizons, whereas conversion signals are inherently sparse, long-delayed, and frequently unattributed. Despite these statistical disparities, both signal types must inform models that operate within strict serving-latency constraints. Prior pre-training approaches address this heterogeneity with a single, undifferentiated encoder applied uniformly across both data streams. We propose DUET (Dual User Embedding Transformers), a framework that explicitly partitions user behavioral data into two domain-coherent streams -- clicks and conversions -- and pre-trains dedicated transformer encoders with architectures tailored to each stream's statistical characteristics: multi-layer self-attention for the dense click stream and interleaved cross- and self-attention for the sparse conversion stream. The resulting complementary embeddings are jointly consumed by a downstream ranker without exceeding serving-latency budgets. Evaluation demonstrates up to 0.38% normalized entropy (NE) reduction relative to the strongest baseline, and A/B test shows consistent improvements in OCVR prediction accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。