@inproceedings{cheng-etal-2026-behavior2trip,
title = "{B}ehavior2{T}rip: Towards Personalized Travel Planning via User Behavior Trajectory",
author = "Cheng, Zihao and
Shan, Yingyu and
Wang, Hongru and
Liu, Zeming and
Wang, Xinyi and
Zhu, Xiangrong and
Guo, Yuhang and
Lin, Wei and
Wang, Yunhong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1509/",
pages = "32110--32131",
ISBN = "979-8-89176-462-0",
abstract = "Travel planning agents assist users in generating personalized travel plans by modeling their individual preferences. Existing agents either rely on explicit user instructions or engage in multi-turn clarification to elicit user preferences. However, both approaches overlook the rich behavioral signals latent in users' past behaviors, which implicitly encode their preferences. This over-reliance on active user input increases interaction burden and limits plan personalization. To bridge this gap, we introduce a new task, **Behavior-Aware Travel Planning**, which infers user preferences directly from past behaviors and generates personalized travel plans. To facilitate research on this task, we introduce **Behavior2Trip**, a benchmark constructed from one of the largest Chinese online travel platforms, comprising 11,400 instances. Each instance represents an average of 39.8 past user behaviors spanning 14 attributes across 5 preference dimensions. We further propose **B2T-Agent**, a reinforcement learning-based agent that leverages user behavior trajectories, interacts with external tools for preference-aligned retrieval, and maintains an internal memory module. Experiments on Behavior2Trip show that GPT-4.1 achieves a full-constraint pass rate of only 0.5{\%} on the hardest tasks, while B2T-Agent built upon Qwen3-8B outperforms all baselines, highlighting the substantial challenge of this task. Moreover, Qwen3-8B trained with B2T-Agent also outperforms GPT-4.1 on the TravelPlanner benchmark, demonstrating strong generalization."
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<abstract>Travel planning agents assist users in generating personalized travel plans by modeling their individual preferences. Existing agents either rely on explicit user instructions or engage in multi-turn clarification to elicit user preferences. However, both approaches overlook the rich behavioral signals latent in users’ past behaviors, which implicitly encode their preferences. This over-reliance on active user input increases interaction burden and limits plan personalization. To bridge this gap, we introduce a new task, **Behavior-Aware Travel Planning**, which infers user preferences directly from past behaviors and generates personalized travel plans. To facilitate research on this task, we introduce **Behavior2Trip**, a benchmark constructed from one of the largest Chinese online travel platforms, comprising 11,400 instances. Each instance represents an average of 39.8 past user behaviors spanning 14 attributes across 5 preference dimensions. We further propose **B2T-Agent**, a reinforcement learning-based agent that leverages user behavior trajectories, interacts with external tools for preference-aligned retrieval, and maintains an internal memory module. Experiments on Behavior2Trip show that GPT-4.1 achieves a full-constraint pass rate of only 0.5% on the hardest tasks, while B2T-Agent built upon Qwen3-8B outperforms all baselines, highlighting the substantial challenge of this task. Moreover, Qwen3-8B trained with B2T-Agent also outperforms GPT-4.1 on the TravelPlanner benchmark, demonstrating strong generalization.</abstract>
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%0 Conference Proceedings
%T Behavior2Trip: Towards Personalized Travel Planning via User Behavior Trajectory
%A Cheng, Zihao
%A Shan, Yingyu
%A Wang, Hongru
%A Liu, Zeming
%A Wang, Xinyi
%A Zhu, Xiangrong
%A Guo, Yuhang
%A Lin, Wei
%A Wang, Yunhong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F cheng-etal-2026-behavior2trip
%X Travel planning agents assist users in generating personalized travel plans by modeling their individual preferences. Existing agents either rely on explicit user instructions or engage in multi-turn clarification to elicit user preferences. However, both approaches overlook the rich behavioral signals latent in users’ past behaviors, which implicitly encode their preferences. This over-reliance on active user input increases interaction burden and limits plan personalization. To bridge this gap, we introduce a new task, **Behavior-Aware Travel Planning**, which infers user preferences directly from past behaviors and generates personalized travel plans. To facilitate research on this task, we introduce **Behavior2Trip**, a benchmark constructed from one of the largest Chinese online travel platforms, comprising 11,400 instances. Each instance represents an average of 39.8 past user behaviors spanning 14 attributes across 5 preference dimensions. We further propose **B2T-Agent**, a reinforcement learning-based agent that leverages user behavior trajectories, interacts with external tools for preference-aligned retrieval, and maintains an internal memory module. Experiments on Behavior2Trip show that GPT-4.1 achieves a full-constraint pass rate of only 0.5% on the hardest tasks, while B2T-Agent built upon Qwen3-8B outperforms all baselines, highlighting the substantial challenge of this task. Moreover, Qwen3-8B trained with B2T-Agent also outperforms GPT-4.1 on the TravelPlanner benchmark, demonstrating strong generalization.
%U https://aclanthology.org/2026.findings-emnlp.1509/
%P 32110-32131
Markdown (Informal)
[Behavior2Trip: Towards Personalized Travel Planning via User Behavior Trajectory](https://aclanthology.org/2026.findings-emnlp.1509/) (Cheng et al., Findings 2026)
ACL
- Zihao Cheng, Yingyu Shan, Hongru Wang, Zeming Liu, Xinyi Wang, Xiangrong Zhu, Yuhang Guo, Wei Lin, and Yunhong Wang. 2026. Behavior2Trip: Towards Personalized Travel Planning via User Behavior Trajectory. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 32110–32131, Budapest, Hungary. Association for Computational Linguistics.