mlsanityData to 14 Oct 2026

JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling

arXiv 2606.19108 ↗First posted 17 Jun 2026cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 44 references that are in the map. First posted on arXiv 17 Jun 2026.

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References · 29

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17 more
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  17. 2024Semantic Retrieval at Walmartcited by 3

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