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Article

A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos

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Nansha Branch, Guangzhou Urban Planning and Design Survey Research Institute, Guangzhou 510060, China
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Geographic Information Center, Guangzhou Urban Planning and Design Survey Research Institute, Guangzhou 510060, China
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School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
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Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University, Wuhan 430079, China
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Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan 430079, China
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Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(1), 20; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010020
Received: 13 November 2020 / Revised: 16 December 2020 / Accepted: 2 January 2021 / Published: 8 January 2021
(This article belongs to the Special Issue Geovisualization and Social Media)
The rapid development of social media data, including geotagged photos, has benefited the research of tourism geography; additionally, tourists’ increasing demand for personalized travel has encouraged more researchers to pay attention to tourism recommendation models. However, few studies have comprehensively considered the content and contextual information that may influence the recommendation accuracy, especially tourist attractions’ visual content due to redundant and noisy geotagged photos; therefore, we propose a tourist attraction recommendation model for Flickr-geotagged photos which fuses spatial, temporal, and visual embeddings (STVE). After spatial clustering and extracting visual embeddings of tourist attractions’ representative images, the spatial and temporal embeddings are modeled with the Word2Vec negative sampling strategy, and the visual embeddings are fused with Matrix Factorization and Bayesian Personalized Ranking. The combination of these two parts comprises our proposed STVE model. The experimental results demonstrate that our STVE model outperforms other baseline models. We also analyzed the parameter sensitivity and component performance to prove the performance superiority of our model. View Full-Text
Keywords: tourist attractions; geotagged photos; matrix factorization; Word2Vec; visual content tourist attractions; geotagged photos; matrix factorization; Word2Vec; visual content
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MDPI and ACS Style

Han, S.; Liu, C.; Chen, K.; Gui, D.; Du, Q. A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos. ISPRS Int. J. Geo-Inf. 2021, 10, 20. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010020

AMA Style

Han S, Liu C, Chen K, Gui D, Du Q. A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos. ISPRS International Journal of Geo-Information. 2021; 10(1):20. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010020

Chicago/Turabian Style

Han, Shanshan, Cuiming Liu, Keyun Chen, Dawei Gui, and Qingyun Du. 2021. "A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos" ISPRS International Journal of Geo-Information 10, no. 1: 20. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010020

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