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arxiv: 2606.19108 · v1 · pith:FOXAMZQTnew · submitted 2026-06-17 · 💻 cs.LG

JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling

Daochen Zha , Chun How Tan , Xin Liu , Bin Xu , Han Zhao , Xiaowei Liu , Tracy Yu , Hui Gao
show 4 more authors
Huiji Gao Liwei He Stephanie Moyerman Sanjeev Katariya
This is my paper

Pith reviewed 2026-06-26 21:12 UTC · model grok-4.3

classification 💻 cs.LG
keywords sequence modelingrecommendation systemssearch rankingguest journeyproduction deploymentA/B testingAirbnb
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The pith

JourneyFormer applies sequence modeling to long, exploratory Airbnb guest sequences with sparse booking labels, enabling production deployment that improves ranking metrics and business KPIs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows how sequence models can be adapted for Airbnb search ranking despite the practical difficulties of lengthy guest event histories and rare booking outcomes. It walks through concrete choices in selecting which events to include, embedding identifiers, choosing the model structure, attributing labels, and speeding up training and serving. These choices allowed the model to move from research to live use on two surfaces, where it lifted both offline ranking scores and online business results measured by A/B tests. A sympathetic reader would see this as evidence that sequence modeling can be made workable at scale for complex user journeys rather than remaining a theoretical tool.

Core claim

JourneyFormer is a sequence model built for Airbnb guest journeys that incorporates targeted decisions on event selection, ID embeddings, architecture, and label attribution, plus acceleration techniques for training and inference; once deployed in production it delivered gains in offline ranking metrics and statistically significant improvements in key business metrics across two production surfaces.

What carries the argument

JourneyFormer sequence model that encodes sequences of guest events to infer booking intentions for search ranking, supported by the listed design choices for data and modeling.

If this is right

  • Sequence models become practical for production ranking when event selection, embedding strategy, architecture, and label rules are jointly tuned for length and sparsity.
  • Acceleration methods for training and inference allow the model to meet the latency and throughput needs of live search surfaces.
  • Offline ranking improvements translate to measurable online business metric gains when the model is deployed on real user traffic.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same design pattern could be tested on other platforms where user sequences are long and purchase events are infrequent.
  • Label attribution rules developed here might reduce noise in any ranking task that relies on delayed or sparse conversion signals.
  • The emphasis on production constraints suggests that future sequence models for recommendation will need explicit handling of both data volume and serving cost.

Load-bearing premise

The observed gains in ranking metrics and business KPIs are caused by the specific JourneyFormer design decisions rather than by unrelated system changes or external factors.

What would settle it

An A/B test that swaps only the JourneyFormer component into the existing production system and measures no lift in the reported business metrics would falsify the attribution of gains to the model.

Figures

Figures reproduced from arXiv: 2606.19108 by Bin Xu, Chun How Tan, Daochen Zha, Han Zhao, Hui Gao, Huiji Gao, Liwei He, Sanjeev Katariya, Stephanie Moyerman, Tracy Yu, Xiaowei Liu, Xin Liu.

Figure 1
Figure 1. Figure 1: An example of a guest journey—typically long, exploratory, and complex. Note that this journey is purely hypothetical [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An overview of JourneyFormer. The guest sequence consists of a long-term sequence (events from the past 7 years, excluding frequent events like listing views) and a short-term sequence (frequent events from the past 21 days). The guest sequence, the search query and the retrieved listing candidates use a shared feature pool to construct feature representations. They are then processed by a sequence encoder… view at source ↗
Figure 3
Figure 3. Figure 3: The distribution of event types, with the majority [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Batching of searches during training. 3.4 Listing Ranking Given a sequence embedding e𝑔, a query 𝑞, and the set of retrieved listings L𝑞 = {𝑙1,𝑙2, ...,𝑙𝑛 }, our goal is to train a ranking model that scores each listing within L𝑞. Specifically, at Airbnb, our goal is to rank listings with a higher booking likelihood higher. Label attribution. As shown in [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 7
Figure 7. Figure 7: NDCG w.r.t. the number of long-term (left) and [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: NDCG evaluated up to 1 month after the last date [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: NDCG w.r.t. the unified emebdding table size (left) [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The left figure shows the average number of [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: NDCG w.r.t. the total number of events. We report [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript introduces JourneyFormer, a sequence modeling solution for encoding long, exploratory Airbnb guest journeys in search ranking. It discusses production design choices including guest event selection, ID embeddings, model architecture, label attribution, and strategies to accelerate training and inference. The central claim is successful production deployment, with effectiveness shown via improved offline ranking metrics and significant gains in business metrics from online A/B testing on two production surfaces.

Significance. If the A/B gains can be robustly attributed to the reported design choices rather than external factors, the work would provide a useful case study on deploying sequence models under sparse labels and complex user behavior at scale, offering practical guidance for recommendation systems in travel and e-commerce.

major comments (2)
  1. [Abstract] Abstract: The claim that JourneyFormer produced 'significant gains in key business metrics through online A/B testing across 2 production surfaces' is load-bearing for the central deployment-success assertion, yet the text supplies no description of test isolation, controls for concurrent ranking-system changes, baseline configurations, or statistical significance testing. This prevents verification that observed lifts are attributable to the listed design choices (event selection, ID embeddings, architecture, label attribution) rather than external factors.
  2. [Abstract] Abstract and throughout: No equations, model diagrams, data statistics (e.g., sequence lengths, label sparsity rates), baseline comparisons, or error analysis are provided, so the soundness of the sequence-modeling approach and the attribution of metric improvements cannot be evaluated against the manuscript's own evidence.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comments point by point below, committing to revisions that strengthen the attribution of results and the supporting evidence while respecting the production-focused nature of the work.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that JourneyFormer produced 'significant gains in key business metrics through online A/B testing across 2 production surfaces' is load-bearing for the central deployment-success assertion, yet the text supplies no description of test isolation, controls for concurrent ranking-system changes, baseline configurations, or statistical significance testing. This prevents verification that observed lifts are attributable to the listed design choices (event selection, ID embeddings, architecture, label attribution) rather than external factors.

    Authors: We agree that the abstract claim would be strengthened by explicit details on the A/B testing protocol. In the revision we will add a dedicated online evaluation section describing test isolation procedures, baseline configurations, controls for concurrent system changes, and statistical significance testing. This will allow readers to better assess attribution to the reported design choices. revision: yes

  2. Referee: [Abstract] Abstract and throughout: No equations, model diagrams, data statistics (e.g., sequence lengths, label sparsity rates), baseline comparisons, or error analysis are provided, so the soundness of the sequence-modeling approach and the attribution of metric improvements cannot be evaluated against the manuscript's own evidence.

    Authors: The manuscript emphasizes production deployment challenges rather than novel algorithmic contributions, which is why new equations or architecture diagrams were omitted. We accept that adding empirical support would improve evaluability. The revision will incorporate data statistics (sequence length distributions, label sparsity), baseline comparisons, and error analysis to substantiate the offline and online results. revision: yes

Circularity Check

0 steps flagged

No derivation chain present; empirical deployment results stand on external A/B benchmarks

full rationale

The paper contains no mathematical derivation, equations, or first-principles claims. It describes design choices for a sequence model and reports measured improvements from offline metrics plus online A/B tests on production surfaces. These results are external benchmarks rather than outputs derived from the model inputs by construction. No self-citations, fitted parameters renamed as predictions, or ansatzes appear in the provided text. The central claim is therefore self-contained against the reported empirical evidence and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is an empirical deployment report with no mathematical derivations, free parameters, axioms, or invented entities described in the abstract.

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discussion (0)

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Reference graph

Works this paper leans on

49 extracted references · 4 linked inside Pith

  1. [1]

    Mustafa Abdool, Malay Haldar, Prashant Ramanathan, Tyler Sax, Lanbo Zhang, Aamir Manaswala, Lynn Yang, Bradley Turnbull, Qing Zhang, and Thomas Legrand. 2020. Managing diversity in airbnb search. InProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2952–2960

    2020

  2. [2]

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners.Advances in neural information processing systems33 (2020), 1877–1901

    2020

  3. [3]

    Maarten Buyl, Paul Missault, and Pierre-Antoine Sondag. 2023. Rankformer: Listwise learning-to-rank using listwide labels. InProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3762–3773

    2023

  4. [4]

    Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007. Learning to rank: from pairwise approach to listwise approach. InProceedings of the 24th international conference on Machine learning. 129–136

    2007

  5. [5]

    Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018. Sequential recommendation with user memory networks. InProceedings of the eleventh ACM international conference on web search and data mining. 108–116

    2018

  6. [6]

    Yongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley, and Caiming Xiong. 2022. Intent contrastive learning for sequential recommendation. InProceedings of the ACM web conference 2022. 2172–2182

    2022

  7. [7]

    Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555(2014)

    Pith/arXiv arXiv 2014

  8. [8]

    Benjamin Coleman, Wang-Cheng Kang, Matthew Fahrbach, Ruoxi Wang, Lichan Hong, Ed Chi, and Derek Cheng. 2023. Unified Embedding: Battle-tested feature representations for web-scale ML systems.Advances in Neural Information Processing Systems36 (2023), 56234–56255

    2023

  9. [9]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). 4171–4186

    2019

  10. [10]

    Tim Donkers, Benedikt Loepp, and Jürgen Ziegler. 2017. Sequential user-based recurrent neural network recommendations. InProceedings of the eleventh ACM conference on recommender systems. 152–160

    2017

  11. [11]

    Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Jianfeng Qu, Fuzhen Zhuang, Guan- feng Liu, Yanchi Liu, and Victor S Sheng. 2023. Frequency enhanced hybrid attention network for sequential recommendation. InProceedings of the 46th International ACM SIGIR conference on research and development in information retrieval. 78–88

    2023

  12. [12]

    Ziwei Fan, Zhiwei Liu, Yu Wang, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, and Philip S Yu. 2022. Sequential recommendation via stochastic self-attention. InProceedings of the ACM web conference 2022. 2036–2047

    2022

  13. [13]

    Alex Graves and Alex Graves. 2012. Long short-term memory.Supervised sequence labelling with recurrent neural networks(2012), 37–45

    2012

  14. [14]

    Malay Haldar, Mustafa Abdool, Liwei He, Dillon Davis, Huiji Gao, and Sanjeev Katariya. 2023. Learning To Rank Diversely At Airbnb. InProceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4609–4615

    2023

  15. [15]

    Malay Haldar, Mustafa Abdool, Prashant Ramanathan, Tao Xu, Shulin Yang, Huizhong Duan, Qing Zhang, Nick Barrow-Williams, Bradley C Turnbull, Bren- dan M Collins, et al. 2019. Applying deep learning to airbnb search. Inproceedings of the 25th ACM SIGKDD international conference on knowledge discovery & Data Mining. 1927–1935

    2019

  16. [16]

    Ruining He, Wang-Cheng Kang, and Julian McAuley. 2017. Translation-based recommendation. InProceedings of the eleventh ACM conference on recommender systems. 161–169

    2017

  17. [17]

    Ruining He and Julian McAuley. 2016. Fusing similarity models with markov chains for sparse sequential recommendation. In2016 IEEE 16th international conference on data mining (ICDM). IEEE, 191–200

    2016

  18. [18]

    Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk

  19. [19]

    Session-based recommendations with recurrent neural networks.arXiv preprint arXiv:1511.06939(2015)

    Pith/arXiv arXiv 2015

  20. [20]

    Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recom- mendation. In2018 IEEE international conference on data mining (ICDM). IEEE, 197–206

    2018

  21. [21]

    Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. InProceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1419–1428

    2017

  22. [22]

    Jiacheng Li, Yujie Wang, and Julian McAuley. 2020. Time interval aware self- attention for sequential recommendation. InProceedings of the 13th international conference on web search and data mining. 322–330

    2020

  23. [23]

    Chen Luo, William Headden, Neela Avudaiappan, Haoming Jiang, Tianyu Cao, Qingyu Yin, Yifan Gao, Zheng Li, Rahul Goutam, Haiyang Zhang, et al . 2022. Query attribute recommendation at amazon search. InProceedings of the 16th ACM conference on recommender systems. 506–508

    2022

  24. [24]

    Alessandro Magnani, Feng Liu, Suthee Chaidaroon, Sachin Yadav, Praveen Reddy Suram, Ajit Puthenputhussery, Sijie Chen, Min Xie, Anirudh Kashi, Tony Lee, et al. 2022. Semantic retrieval at walmart. InProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3495–3503

    2022

  25. [25]

    Paul Missault, Arnaud de Myttenaere, Andreas Radler, and Pierre-Antoine Sondag

  26. [26]

    Addressing cold start with dataset transfer in e-commerce learning to rank. (2021)

    2021

  27. [27]

    Nikil Pancha, Andrew Zhai, Jure Leskovec, and Charles Rosenberg. 2022. Pinner- former: Sequence modeling for user representation at pinterest. InProceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. 3702–3712

    2022

  28. [28]

    Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi

  29. [29]

    Inproceedings of the Eleventh ACM Conference on Recommender Systems

    Personalizing session-based recommendations with hierarchical recurrent neural networks. Inproceedings of the Eleventh ACM Conference on Recommender Systems. 130–137

  30. [30]

    Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners.OpenAI blog 1, 8 (2019), 9

    2019

  31. [31]

    Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton. 2007. Restricted Boltz- mann machines for collaborative filtering. InProceedings of the 24th international conference on Machine learning. 791–798

    2007

  32. [32]

    Guy Shani, David Heckerman, and Ronen I Brafman. 2005. An MDP-based recommender system.Journal of machine Learning research6, Sep (2005), 1265– 1295

    2005

  33. [33]

    Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang

  34. [34]

    InProceedings of the 28th ACM international conference on information and knowledge management

    BERT4Rec: Sequential recommendation with bidirectional encoder rep- resentations from transformer. InProceedings of the 28th ACM international conference on information and knowledge management. 1441–1450

  35. [35]

    Chun How Tan, Austin Chan, Malay Haldar, Jie Tang, Xin Liu, Mustafa Abdool, Huiji Gao, Liwei He, and Sanjeev Katariya. 2023. Optimizing airbnb search jour- ney with multi-task learning. InProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4872–4881

    2023

  36. [36]

    Jie Tang, Huiji Gao, Liwei He, and Sanjeev Katariya. 2024. Multi-objective Learning to Rank by Model Distillation. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 5783–5792

    2024

  37. [37]

    Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommenda- tion via convolutional sequence embedding. InProceedings of the eleventh ACM international conference on web search and data mining. 565–573

    2018

  38. [38]

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971(2023)

    Pith/arXiv arXiv 2023

  39. [39]

    Andrew Trotman, Jon Degenhardt, and Surya Kallumadi. 2017. The Architecture of eBay Search.. IneCOM@ SIGIR. 88

    2017

  40. [40]

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need.Advances in neural information processing systems30 (2017)

    2017

  41. [41]

    Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & cross network for ad click predictions. InProceedings of the ADKDD’. 1–7

    2017

  42. [42]

    Liwei Wu, Shuqing Li, Cho-Jui Hsieh, and James Sharpnack. 2020. SSE-PT: Sequential recommendation via personalized transformer. InProceedings of the 14th ACM conference on recommender systems. 328–337

    2020

  43. [43]

    Xue Xia, Pong Eksombatchai, Nikil Pancha, Dhruvil Deven Badani, Po-Wei Wang, Neng Gu, Saurabh Vishwas Joshi, Nazanin Farahpour, Zhiyuan Zhang, and Andrew Zhai. 2023. TransAct: Transformer-based Realtime User Ac- tion Model for Recommendation at Pinterest.Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(2023). https: //a...

    2023

  44. [44]

    Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. A dynamic recurrent model for next basket recommendation. InProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 729–732

    2016

  45. [45]

    Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Michael He, Yin-Hua Lu, and Yu Shi. 2024. Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Genera- tive Recommendations.ArXivabs/2402.17152 (2024). https://api.semanticscholar. org/CorpusID:268033327

    Pith/arXiv arXiv 2024

  46. [46]

    Yipeng Zhang, Xin Wang, Hong Chen, and Wenwu Zhu. 2023. Adaptive disen- tangled transformer for sequential recommendation. InProceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining. 3434–3445

    2023

  47. [47]

    Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. InProceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1059–1068. JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling K...

    2018

  48. [48]

    Peilin Zhou, Qichen Ye, Yueqi Xie, Jingqi Gao, Shoujin Wang, Jae Boum Kim, Chenyu You, and Sunghun Kim. 2023. Attention calibration for transformer- based sequential recommendation. InProceedings of the 32nd ACM international conference on information and knowledge management. 3595–3605

    2023

  49. [49]

    Christos Ziakis, Maro Vlachopoulou, Theodosios Kyrkoudis, and Makrina Karagkiozidou. 2019. Important factors for improving Google search rank.Future internet11, 2 (2019), 32. A Additional Details A.1 Unified Embedding Table We used a unified embedding table with1 .6 × 107 entries and em- bedding dimension 1. For each example, we used 6 seeds for the listi...

    2019