User Transition Pattern Analysis for Travel Route Recommendation

Sun, Jie; Zhuang, Chenyi; Ma, Qiang

doi:10.1587/transinf.2019edp7096

Cited by 6 publications

(2 citation statements)

References 28 publications

(50 reference statements)

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“…Liu et al [35] and Chen et al [2] recommend topics and POIs to tourists by utilizing POIs' textual information. Besides, there are also some works [29]- [31] studying other aspects, such as privacy protection, user transition pattern analysis, and mixed styles of sightseeing.…”

Section: Related Workmentioning

confidence: 99%

RPMTD: A Route Planning Model With Consideration of Tourists’ Distribution

Kong,

Yi,

Wang

et al. 2024

IEEE Access

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The rapid growth of the tourism industry has spurred extensive research into tourist route planning. However, existing studies primarily focus on route planning for individual tourists, leaving a notable gap in addressing multiple tourists planning scenarios. Traditional methods for multiple tourists planning, derived from single tourist frameworks, often prioritize the interests and benefits of tourists. This has led to problems like popularity-biased route planning, which exacerbates overtourism in favored destinations and under-tourism in lesser-known spots, thereby undermining sustainable tourism. To address these challenges, we introduce a multi-agent reinforcement learning (RL) framework specifically designed for multiple tourists route planning, incorporating consideration of tourists' distribution. Our approach consists of two key components: firstly, a novel tourism RL environment that enables interaction with multiple tourists; secondly, a dual-congestion model that considers both local congestion at tourist spots and overall city-wide tourist' distribution. This dual-congestion mechanism formulates the reward system in our multi-agent RL framework. We validate our framework with comprehensive experiments using real-world human mobility data from Kyoto, a globally renowned tourist city. The results show our model's superiority over existing methods in optimizing route rewards and managing tourists' distribution. Additionally, we conducted a user study to examine the effect of our method on tourists' experience, serving as a reference for our future implementation. The findings indicate that our dual-congestion model slightly affects tourists who prefer popular spots. Our discussion highlights the typically non-cooperative relationship between tourism sustainability and tourist self-interest, underscoring the necessity for a trade-off between them in practical applications. Significantly, our model demonstrates the potential to transform this non-cooperative relationship into a cooperative dynamic. INDEX TERMSMultiple Tourists Route Planning, Unbiased Route Planning, Sustainable Tourism Sightseeing for a large number of tourists [5].

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Section: Related Workmentioning

confidence: 99%

RPMTD: A Route Planning Model With Consideration of Tourists’ Distribution

Kong,

Yi,

Wang

et al. 2024

IEEE Access

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“…Therefore, when recommending hotel accommodation for the tourists, the first step is to search for the optimal tourist attractions, and then recommend the optimal hotels for the tourists based on their accommodation requirements [22,23]. Based on the analysis, we construct a tourist attraction SAFS model based on the urban hotels and search for the tour routes with travel costs starting from the hotels to visit all the recommended tourist attractions, according to which the hotels that generate the lowest travel costs are determined [24].…”

Section: Har Algorithm Based On Safsmentioning

confidence: 99%

Tourism Hotel Accommodation Recommendation Algorithm Based on the Cellular Space-Improved Divisive Analysis (CS-IDIANA) Clustering Model

Zhou,

Peng,

Wen

et al. 2023

Electronics

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On the basis of analyzing the problems concerning hotel accommodation recommendation (HAR), this paper constructs a tourism HAR algorithm based on the CS-IDIANA clustering model (cellular space-improved divisive analysis). The algorithm integrates the cellular space model with DIANA, and takes the tourist attractions and the travel route costs as the research background and constraint conditions. Considering the feature attributes and spatial attributes of the tourist attractions, the tourist attraction recommendation algorithm based on the CS-IDIANA clustering model is established, then the HAR algorithm based on the spatial accessibility and route cost is constructed, with the constraints of the spatial accessibility field strength (SAFS) between the hotels and attractions and the travel route costs between the hotels and attractions. The experiment selects the tourism city Zhengzhou as the research object, and the experimental results are analyzed in four dimensions: the clustering results, the recommendation field strength of the tourist attractions, the hotel SAFS and the HAR results. The experiment proves that the proposed algorithm can find the best matched tourist attractions for tourists and the hotels with the lowest tour route cost based on the constraint conditions. Compared to the suboptimal hotels, the route costs are reduced by 5.67% and 9.63%, respectively. Compared to the hotel with the highest route cost, it reduces the travel costs by 29.23%. Compared with the two commonly used recommendation methods, the UCFR (user-based collaborative filtering recommendation) and ICFR (item-based collaborative filtering recommendation), the proposed CSIDR (CS-IDIANA recommendation) has a higher accuracy and recall rate.

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MERIHARI-Area Tour Planning by Considering Regional Characteristics

Moritake

Kasahara

2023

Lecture Notes in Computer Science

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User Transition Pattern Analysis for Travel Route Recommendation

Cited by 6 publications

References 28 publications

RPMTD: A Route Planning Model With Consideration of Tourists’ Distribution

RPMTD: A Route Planning Model With Consideration of Tourists’ Distribution

Tourism Hotel Accommodation Recommendation Algorithm Based on the Cellular Space-Improved Divisive Analysis (CS-IDIANA) Clustering Model

MERIHARI-Area Tour Planning by Considering Regional Characteristics

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