用序列模型分析用户预订路径，提升爱彼迎搜索排序效果
JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling
- 构建旅程序列模型，融合用户行为与上下文信息
- 上线后显著提升离线指标与核心业务指标
- 适合推荐系统、搜索排序方向的工程落地研究
序列建模在推荐与排序算法中日益流行，因其能捕捉用户历史行为并推断意图。然而在实际部署中，由于序列复杂且标签稀疏，挑战重重。以爱彼迎为例，用户行为序列往往长而探索性强，且仅关注预订这一稀疏标签。为此，本文深入探讨了生产环境中的关键设计问题，提出JourneyFormer序列建模方案用于搜索排序。涵盖用户事件选择、ID嵌入、模型架构与标签归属等设计考量，并提出多项加速训练与推理的定制策略。该模型已成功部署于爱彼迎生产环境，不仅在离线排名指标上表现优异，更通过跨两个生产表面的在线A/B测试，带来显著业务指标提升。
原文摘要 · Abstract (English)
Sequence modeling has become increasingly popular in recommendation and ranking algorithms, owing to its capacity to model users' historical behaviors and infer user intentions. Despite its theoretical simplicity, the practical deployment of a sequence model in production is non-trivial due to complexity of the sequence and sparse labels. For example, in Airbnb, guest sequences are often long, exploratory and complex, and we focus on booking labels, which are sparse. As such, we are often required to make various design decisions regarding data and modeling to strike a balance between effectiveness and scalability. This work delved into these production challenges and deployed JourneyFormer, a sequence modeling solution for search ranking at Airbnb. We detail crucial design considerations, covering aspects such as guest event selection, ID embeddings, model architecture, and label attribution. Additionally, we describe several tailored strategies to accelerate model training and inference. JourneyFormer has been successfully deployed within Airbnb's production, where its effectiveness and impact have been evidenced not only by improved offline ranking metrics but also by significant gains in key business metrics through online A/B testing across 2 production surfaces.
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