Airbnb搞了个JourneyFormer，专治搜索排序中又长又乱的用户序列，实测线上A/B测试效果很不错。
JourneyFormer是Airbnb提出的序列建模解决方案，用于处理搜索排序中客人序列长、探索性强且标签稀疏的问题。模型设计涵盖事件选择、ID嵌入、模型架构和标签归因等关键环节，并采用专门策略加速训练和推理。JourneyFormer已部署到Airbnb生产环境，离线排名指标和线上A/B测试（覆盖2个生产表面）均显示关键业务指标显著提升。
JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling
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.