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TravelAgent: An AI Assistant for Personalized Travel Planning

Aili Chen, Xuyang Ge, Ziquan Fu, Yanghua Xiao, Jiangjie Chen

arXiv:2409.08069v1cs.AIcs.CL

TL;DR

Dynamic travel planning must balance constraint-respecting itineraries, comprehensive real-time information, and personalization, yet existing systems and LLM planning methods struggle to meet these demands. TravelAgent combines LLM-powered tools, recommendation, planning, and memory; human evaluations report it surpasses GPT-4+ across the three criteria, while simulated-user tests report high rating-prediction accuracy.

  • Problem

    Existing travel systems depend on manual selection or rule-based combinations, and LLM planners struggle with real-world constraints and real-time information needed for rational, comprehensive, personalized itineraries.

    Source: §1 · p. 1

  • Method

    TravelAgent combines tool use, personalized recommendation, planning, and memory, drawing on real-time information and user history to generate travel services.

    Source: §1 · p. 2, §3.2 · p. 3, §3.3 · p. 4

  • Results

    Human case studies report TravelAgent surpassing GPT-4+ across the three criteria, and simulated-user tests report high attraction-rating prediction accuracy.

    Source: §1 · p. 2, §4.2 · p. 6

  • Takeaways & Limitations

    The system delivers rational, comprehensive, and personalized travel services in the evaluated travel scenarios.

    Source: §1 · p. 2, §5 · p. 6

    TravelAgent depends heavily on the quality of dynamic real-time data sources, whose inaccuracies can produce suboptimal recommendations and impractical plans.

    Source: Limitations · p. 7

Abstract

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As global tourism expands and artificial intelligence technology advances, intelligent travel planning services have emerged as a significant research focus. Within dynamic real-world travel scenarios with multi-dimensional constraints, services that support users in automatically creating practical and customized travel itineraries must address three key objectives: Rationality, Comprehensiveness, and Personalization. However, existing systems with rule-based combinations or LLM-based planning methods struggle to fully satisfy these criteria. To overcome the challenges, we introduce TravelAgent, a travel planning system powered by large language models (LLMs) designed to provide reasonable, comprehensive, and personalized travel itineraries grounded in dynamic scenarios. TravelAgent comprises four modules: Tool-usage, Recommendation, Planning, and Memory Module. We evaluate TravelAgent's performance with human and simulated users, demonstrating its overall effectiveness in three criteria and confirming the accuracy of personalized recommendations.

1 Introduction

TravelAgent addresses the challenge of generating practical, customized itineraries in dynamic travel scenarios by targeting Rationality, Comprehensiveness, and Personalization. Its modular LLM-based system is evaluated with human and simulated users.

1 Introduction

  • Existing platforms rely on manual selection or rule-based combinations and cannot autonomously generate itineraries that adapt to dynamic scenarios..Source: §1 · p. 1
  • Travel planning requires handling multiple constraints and real-time information, while current LLMs and planning strategies struggle to produce viable plans meeting all constraints..Source: §1 · p. 1
  • TravelAgent combines constraint modeling, real-time tools, personalized recommendation, planning, and memory to address the three travel-service criteria.The paper identifies Rationality, Comprehensiveness, and Personalization as the objectives for travel planning.Source: §1 · p. 2
  • Human case studies report that TravelAgent surpasses the GPT-4+ agent across Rationality, Comprehensiveness, and Personalization.Simulated-user evaluation also examines behavioral prediction for attraction recommendations.Source: §1 · p. 2

2 Related Work

Prior work on LLM-based agents explores planning, memory, and tool use, while LLM recommendation methods include direct recommendation and profile-based decoding. The section identifies limitations in adaptation and capturing long-term preferences.

2 Related Work

  • LLM-agent planning research uses task decomposition or step-by-step reasoning for dynamic planning [Shen et al., 2024; Wang et al., 2023; Singh et al., 2023; Wei et al., 2022; Yao et al., 2024; Besta et al., 2024; Chen et al., 2022; Yao et al., 2022].Related work also studies memory construction and tool use to leverage historical and external resources [Zhong et al., 2024; Liu et al., 2023b; Hu et al., 2023; Zheng et al., 2023; Huang et al., 2023; Schick et al., 2024; Qin et al., 2023; Lu et al., 2024; Wang et al., 2024a; Yuan et al., 2024].Source: §2 · p. 2
  • LLM recommendation approaches include direct recommendation from behavioral histories and decoding a profile from the last interaction for later recommendations [Dai et al., 2023; Hou et al., 2024; Kang et al., 2023; Wang and Lim, 2023; Zhou et al., 2024; Yang et al., 2023; Liu et al., 2023a]..Source: §2 · p. 2
  • Both recommendation approaches are described as limited in capturing long-term preferences across multiple interactions, resulting in suboptimal effectiveness..Source: §2 · p. 2

3 The TravelAgent System Design

TravelAgent coordinates constraint modeling, real-time tools, personalized recommendations, budget and route planning, and memory. Its components use current inputs and learned insights to adapt recommendations and itineraries.

3 The TravelAgent System Design

  • TravelAgent combines tool use, recommendation, planning, and memory, with budget planning guiding routes that account for time and geographic constraints..Source: §3 · pp. 2–3

3.1 Constraints Modeling

  • Travel constraints include user- and scenario-specific hard constraints, cross-scenario soft constraints learned from history, and commonsense constraints such as avoiding repeated restaurants.Hard constraints must be followed; soft constraints evolve with interactions, while commonsense constraints apply across users and scenarios.Source: §3.1 · p. 3, Figure 1 · p. 3

3.2 Tool-usage Module

  • The Tool-usage Module combines live API data with arithmetic distance and time tools; hard constraints filter parameters before targeted tools are called..Source: §3.2 · p. 3, Figure 2 · p. 3

3.3 Recommendation Module

  • The Recommendation Module uses user constraints, real-time information, history insights, and generalized knowledge to generate recommendations and update learned insights from feedback.Its data-driven framework covers budget, city, flights, hotels, restaurants, and attractions.Source: §3.3 · p. 4, Figure 3 · p. 4

3.4 Planning Module

  • Budget planning allocates funds across six categories, while the Route Planner dynamically selects attractions using remaining time, visit windows, and travel logistics.Its attraction scores combine return-time, optimal-visit-time, and reserve-time components.Source: §3.4 · pp. 4–5

3.5 Memory Module

  • Short-term memory stores recent interactions and contextual travel information, while long-term memory accumulates stable preferences; Figure 5 reports cold-start and error-control trends with and without behavior history..Source: §3.5 · p. 5, Figure 5 · p. 6

4 Evaluation

The evaluation uses human case studies to assess itinerary quality and simulated users to assess attraction-rating prediction. It reports that TravelAgent surpasses GPT-4+ across three criteria and achieves high prediction accuracy.

4.1 Overall Evaluation

  • Human case studies report that TravelAgent surpasses the GPT-4+ agent across Rationality, Comprehensiveness, and Personalization..Source: §4.1 · p. 6
  • The human evaluation covers 20 travel scenarios, with users rating both agents from 1 to 10 on the three criteria.Average scores are presented in Table 1; the supplied passage does not give the table values.Source: §4.1 · p. 6

4.2 Personalization Evaluation

  • In simulated-user tests, TravelAgent predicts ratings for 20 attractions per city across 10 scenarios and is compared with Direct Recommend and Decode and Encode using MAE and RMSE.The passage reports high prediction accuracy over interactions but gives no metric values.Source: §4.2 · p. 6

5 Conclusion

TravelAgent combines constraint-aware planning, personalized recommendations, real-time data processing, and dynamic memory for travel scenarios with multiple constraints. The authors assess it through simulated user rating prediction and real-world human case studies, and report rational, comprehensive, and personalized services.

  • TravelAgent combines tailored spatiotemporal-aware algorithms, agent-based personalized online recommendations, real-time data processing, and dynamic memory.Source: §5 · p. 6
  • The authors assess TravelAgent through simulated user rating prediction and real-world human case studies.Source: §5 · p. 6
  • The system delivers rational, comprehensive, and personalized travel services.Source: §5 · p. 6

Limitations

The study identifies dependence on dynamic real-time data quality and the need to explore personalization using more detailed user information. Outdated flight information can yield suboptimal recommendations and impractical plans.

  • Outdated real-time data, such as flight information, can produce suboptimal recommendations and impractical final plans.Source: Limitations · p. 7
  • Further work is needed to explore personalized activities using more detailed user personalization information.Source: Limitations · p. 7

A Tool-usage Module Details

Table 2 lists tools in Personalized TravelAgent.

B Long-term History Insight Examples

The examples show historical user preferences alongside commonsense constraints for planning. They include preferences for affordable, family-suitable attractions and practical considerations such as heat and travel time.

C Baseline Recommendation Prompts

The appendix shows baseline recommendation prompts and sample travel input forms, illustrating how user profiles, behavioral histories, candidate items, and trip details are supplied.

C Baseline Recommendation Prompts

  • Baseline rating prompts ask for scores on candidate items using prior user ratings, with the number and order of choices matching the candidates.A separate prompt asks for reasons behind likes or dislikes based on rated items.Source: C Baseline Recommendation Prompts · p. 11
  • A profile-only rating prompt presents a user profile and candidate items, while another combines the profile with previous user behavior.Both ask for scores out of five.Source: C Baseline Recommendation Prompts · p. 11

D Input Form Cases

  • The sample input forms specify departure and destination, travel dates, party composition, children’s ages, and restaurant preferences.Examples include routes within China, Europe, the UAE, Japan, and the United States.Source: D Input Form Cases · p. 12
  • The remaining examples include Paris–Lyon, London–Edinburgh, Sydney–Melbourne, and Toronto–Vancouver trips with dates and party details.Source: D Input Form Cases · p. 12
  • Berlin–Munich, Madrid–Barcelona, and Rio de Janeiro–São Paulo forms further vary travel dates, group composition, and restaurant preferences.Source: D Input Form Cases · p. 12
  • Further examples vary party size and route, including trips between Helsinki and Tampere, Amsterdam and Rotterdam, and São Paulo and Rio de Janeiro.The forms also specify children’s ages and restaurant preferences.Source: D Input Form Cases · p. 12
  • Other sample forms cover Bangkok–Chiang Mai, New York–Los Angeles, and Vienna–Salzburg trips, with dates, family details, and cuisine preferences.Source: D Input Form Cases · p. 12

E Human Evaluation Dimensions

Table 3 identifies the evaluation dimensions human evaluators use for Rationality, Comprehensiveness, and Personalization.

  • Table 3 presents evaluation dimensions for human assessment of Rationality, Comprehensiveness, and Personalization.Source: Table 3 · p. 13

F A Case Example of TravelAgent

The case example includes travel-scenario and city-information examples, recommendation outputs, and a dated itinerary with transport, lodging, attractions, and meals.

  • The case materials identify a JSON travel-scenario example, Shanghai city information, and an example of fund allocation with reasons.Source: Table 4 · p. 14, Table 5 · p. 14, Table 6 · p. 15
  • The supplied travel information includes seasonal advice and safety tips for Shanghai, alongside a section identifying recommended outbound flights and attractions.Source: F A Case Example of TravelAgent · pp. 14–16
  • The recommendation output gives attraction details and personalized reasons for Yu Garden and People’s Square, including suggested durations and play-time ranges.Source: F A Case Example of TravelAgent · p. 16
  • The dated itinerary specifies a Beijing–Shanghai flight, hostel check-in, dinner, and visits such as Waibaidu Bridge, Yu Garden, and the aquarium.The entries include times and attraction details; the flight passage gives its duration and round-trip price.Source: F A Case Example of TravelAgent · pp. 16–17
  • Later itinerary entries schedule visits to People’s Square, the Former Residence of Sun Yat-Sen, Jade Buddha Temple, and the Bund, followed by a return flight.The final-day passages also include hotel checkout and flight details.Source: F A Case Example of TravelAgent · pp. 18–19

G A Case Example of GPT-4+ agent (Baseline)

The baseline agent example proposes a family-oriented Shanghai itinerary spanning arrival, Disneyland, city attractions, and return travel.

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