TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Trajectory Data

Chen, Chao; Zhang, Daqing; Wang, Yasha; Huang, Hongyu

doi:10.1007/978-981-16-0178-1_10

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…For example, Jiang et al (2016) build a route recommendation system by automatically extracting POI admission fees, opening hours, and visiting seasons from geo-tagged photographs and travel websites. Some other researches focus on optimizing the tour sequence automatically by considering various factors, such as the traffic conditions (Chen et al 2021;Gavalas et al 2015), the level of POI category (Gionis et al 2014), and the uncertainty of travel time (Liebig et al 2014(Liebig et al , 2017. However, the automatic methods are often plagued by the poor interpretability of their black-box models, making it difficult to trust and adjust the recommended results.…”

Section: Tourism Route Planningmentioning

confidence: 99%

TriPlan: an interactive visual analytics approach for better tourism route planning

Zhang

Pang

Wen

et al. 2022

J Vis

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Continuous research and development of novel tourism routes is necessary for tourism service providers to improve the tourist experience and industrial competitiveness. However, the route planning is cumbersome due to the time-consuming, extensive, and costly field study. Most of the existing route planning studies focus on recommending tourism routes for users based on attraction characteristics or tourist behavior features, which are generally unexplainable due to the black-box approaches they use. Other solutions allow users to customize itineraries through an interactive interface but often lack guidance from the aspect of route evaluation or destination image perception. In this paper, we thoroughly discuss the requirements and design tasks with domain experts and propose TriPlan, an interactive visual analytics system that provides intuitive planning guidance for tourism product developers. We design and improve multiple coordinated visualizations to facilitate analysis from the perspectives of overall route pattern and individual destination image. We also develop a hierarchical planning view to display the structural information of a plan. In addition, we introduce an automatic route optimization algorithm and multiple interactions to assist users in optimizing and adjusting the itineraries. Finally, we evaluate the usability and effectiveness of our system through three case studies and quantitative and qualitative interviews with the domain experts on real-world datasets. Graphical abstract Electronic supplementary material The online version of this article (10.1007/s12650-022-00861-8) contains supplementary material, which is available to authorized users.

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Section: Tourism Route Planningmentioning

confidence: 99%

TriPlan: an interactive visual analytics approach for better tourism route planning

Zhang

Pang

Wen

et al. 2022

J Vis

View full text Add to dashboard Cite

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Sentiment analysis algorithm using contrastive learning and adversarial training for POI recommendation

Shaowei

et al. 2023

Soc. Netw. Anal. Min.

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Finding a suitable POI based on the user’s needs and intentions is a complex decision-making process. Obtaining valuable information from the vast amount of social media data and using it for travel recommendations is a challenging issue. Traditional POI recommendation algorithms do not fully take into account the true feelings of customers about tourist attractions implied in social media data because they usually require a large amount of tagged travel commentary data. This study presents an aspect-based sentiment analysis model and POI recommendation method to accurately capture sentiment information contained in social media data with a small amount of tagged data. The pre-training model BERT is used to obtain the embedded representation of words that fuse the semantic information of the text. Using contrastive learning, point clusters belonging to the same class in the embedded space of words are pulled together, and sample clusters from different classes are separated. The potential relationship between comment ratings and their impact on user perception is analyzed, and the best performance formula for the loss function is determined. The test accuracy and F1-Score of the model in the experiment improved by 13.03% and 12.23%, respectively, compared to the BERT base model. POI recommendation validation is performed using a variety of recommendation algorithms. The experimental results show that the addition of aspect-based sentiment attributes can effectively improve the accuracy of recommendations.

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TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Trajectory Data

Cited by 2 publications

References 9 publications

TriPlan: an interactive visual analytics approach for better tourism route planning

TriPlan: an interactive visual analytics approach for better tourism route planning

Sentiment analysis algorithm using contrastive learning and adversarial training for POI recommendation

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