Personalized tourist route recommendation model with a trajectory understanding via neural networks

Mou, Naixia; Jiang, Qi; Zhang, Lingxian; Niu, Jiqiang; Zheng, Yunhao; Wang, Yanci; Yang, Tengfei

doi:10.1080/17538947.2022.2130456

Cited by 7 publications

(1 citation statement)

References 79 publications

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“…These technical models can to some extent meet the line planning requirements of users. But with the diversification of demand, these technologies have also exposed issues such as slow real-time performance, poor accuracy, and low interactivity [3]. With the continuous development of trajectory mining technology, it is widely used in location services, logistics management, and other areas due to its superior real-time data monitoring and efficient data-driven characteristics [4].…”

Section: Introductionmentioning

confidence: 99%

Personalized Recommendation Algorithm Based on Trajectory Mining Model in Intelligent Travel Route Planning

Shi,

Sun

2024

IJACSA

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With the increasing demand for personalized travel, traditional travel route planning methods are no longer able to meet the diverse needs of users. In view of this, on the ground of the analysis of user trajectory data at the temporal and spatial levels, a new scenic spot recommendation model is proposed by combining personalized recommendation algorithms. Meanwhile, improved genetic algorithm and minimum spanning tree algorithm were introduced to adjust the structure of the personalized recommendation model. After matching the visit sequence of scenic spots, the final new personalized tourism route recommendation model was proposed. The experiment demonstrates that the optimal pause time for the personalized scenic spot recommendation model is 45 minutes, the pause distance is 15 meters, and the clustering radius is 500 meters. And the model has the highest accuracy in the Tok-10 testing environment, with a maximum value of 90%. In addition, the new personalized tourism route recommendation model has the highest accuracy of 85.6%, the highest recall rate of 88.7%, the highest F1 value of 92.4%, and an average convergence rate of 88.9%. In summary, the new scenic spot and route recommendation model proposed in the study can achieve more intelligent and personalized travel route planning, providing new guidance for the intelligent development of travel route recommendation.

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Section: Introductionmentioning

confidence: 99%

Personalized Recommendation Algorithm Based on Trajectory Mining Model in Intelligent Travel Route Planning

Shi,

Sun

2024

IJACSA

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Machine learning applied to tourism: A systematic review

Núñez,

Gómez‐Pulido,

Ramírez

2024

WIREs Data Min & Knowl

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The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper understanding of the progress of this field. Thus, different approaches in the field of tourism will be analyzed, such as planning, forecasting, recommendation, prevention, and security, among others. As a result of this analysis, among other findings, the greater impact of supervised learning in the field of tourism, and more specifically those techniques based on neural networks, has been confirmed. The results of this study would allow researchers not only to have the most up‐to‐date and accurate overview of the application of machine learning in tourism, but also to identify the most appropriate techniques to apply to their domain of interest, as well as other similar approaches with which to compare their own solutions.This article is categorized under: Application Areas > Society and Culture Technologies > Machine Learning Application Areas > Business and Industry

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A Heuristic Framework for Personalized Route Recommendation Based on Convolutional Neural Networks

Zhang,

Liu,

Zhang

et al. 2023

PRICAI 2023: Trends in Artificial Intelligence

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Personalized tourist route recommendation model with a trajectory understanding via neural networks

Cited by 7 publications

References 79 publications

Personalized Recommendation Algorithm Based on Trajectory Mining Model in Intelligent Travel Route Planning

Personalized Recommendation Algorithm Based on Trajectory Mining Model in Intelligent Travel Route Planning

Machine learning applied to tourism: A systematic review

A Heuristic Framework for Personalized Route Recommendation Based on Convolutional Neural Networks

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