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This paper introduces Behavior2Trip, a benchmark for personalized travel planning that leverages user behavior trajectories to infer preferences, addressing the limitations of existing methods that depend on explicit user input. The authors present B2T-Agent, a reinforcement learning-based agent that utilizes these behavioral signals and integrates external tools for preference-aligned retrieval while maintaining an internal memory module. Experimental results reveal that B2T-Agent significantly outperforms traditional baselines and demonstrates superior generalization capabilities compared to GPT-4.1 on the TravelPlanner benchmark, underscoring the effectiveness of behavior-aware approaches in travel planning.
By harnessing user behavior trajectories, B2T-Agent dramatically enhances travel plan personalization, outperforming leading models like GPT-4.1.
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. Code and data are available at https://github.com/BUAA-IRIP-LLM/Behavior2Trip
