HetSpot: Analyzing Tourist Spot Popularity with Heterogeneous Graph Neural Network
Abstract
Tourism spot popularity prediction forecasts the prosperity of tourism destinations using heterogeneous information. Understanding the popularity of a spot can aid city planning and tourism site renovations. However, previous works overlooked interpretability in this task. Moreover, they only relied on a small portion of information on tourism spots and did not fully exploit the potential of Graph Neural Networks (GNNs) in tourism spot popularity prediction. To address the aforementioned problems, we propose a novel Heterogeneous GNN model, which we call HetSpot. By representing multimodal tourism spot information as a heterogeneous graph, HetSpot captures diverse cross modal information effectively to predict spot popularity. Moreover, we introduce an interpretation method for post-hoc analysis. Experimental evaluations on a large-scale Japanese tourism spots dataset show the superior performance of HetSpot. It achieves a high correlation value of 0.82 between our prediction and the actual tourist spot popularity, surpassing state-of-the-art models. Furthermore, the qualitative explanation results align well with our intuition based on experimental findings. The code and URL links used to construct the dataset are available online at https://github.com/HiromasaYamanishi/HetSpot
References
[1]
Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison. Andreas Köpf Edward, Yang Zach, DeVito Martin, Raison Alykhan, Tejani Sasank, Chilamkurthy Benoit, Steiner Lu, Fang Junjie Bai, Adam Paszke, Sam Gross and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Proceedings of Advances in Neural Information Processing Systems 32. 8024-8035.
[2]
Ananthram Swami, Chuxu Zhang and Nitesh V Chawla. 2019. Shne: Representation learning for semantic-associated heterogeneous networks. In Proceedings of the twelfth ACM International Conference on Web Search and Data Mining. 690–698.
[3]
Chao Huang, Ananthram Swami, Chuxu Zhang, Dongjin Song and Nitesh V Chawla. 2019. Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 793–803.
[4]
Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine Learning 20 (1995), 273–297.
[5]
Andrew Y. Ng, David M. Blei and Michael I. Jordan. 2003. Latent dirichlet allocation. Machine Learning 3 (2003), 993–1022.
[6]
Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, Fabian Pedregosa, Gael Varoquaux and David Cournapeau. 2011. Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research 12 (Oct 2011), 2825–2830.
[7]
Matthias Fey and Jan E. Lenssen. 2019. Fast graph representation learning with PyTorch Geometric. In International Conference on Learning Representations Workshop. 8024–8035.
[8]
Sandro Formica and Muzaffer Uysal. 2006. Destination Attractiveness Based on Supply and Demand Evaluations: An Analytical Framework. Tourism Analysis 44, 4 (2006), 418–430.
[9]
Aditya Grover and Jure Leskovec. 2016. Node2vec: Scalable feature learning for networks. In In Proceedings of 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 855–864.
[10]
Linda Kaufma, Alex Smola, Harris Drucker, Chris J.C. Burge and Vladimir Vapoik. 1996. Support Vector Regression Machines. In Proceedings of Advances in Neural Information Processing Systems 9. 155–161.
[11]
Sepp Hochreiter and Jurgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735–1780.
[12]
Yangzhou Hu and J.R. Brent Ritchie. 1993. Measuring Destination Attractiveness: A Contextual Approach. Journal of Travel Research 32, 2 (1993), 25–34.
[13]
Zijia Miao, Binjie Zhang, Qi An, Jiakai Tang, Jiahui Jin and Jinghui Zhang. 2019. Region-aware POI recommendation with semantic spatial graph. In Proceedings of IEEE 24th International Conference on Computer Supported Cooperative Work in Design. 214–219.
[14]
Yibo Sun Miao, Fan Zhengjie, Huang Chunyuan Yuan, Jizhou Huang, Haifeng Wang and Yawen Li. 2021. Hgamn: Heterogeneous graph attention matching network for multilingual poi retrieval at baidu maps. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery Data Mining. 3032–3040.
[15]
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 248–255.
[16]
Shaoqing Ren Kaiming He, Xiangyu Zhang and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 770–778.
[17]
Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization”, In Proceedings of International Conference on Learning Representations.
[18]
Sameer Singh, Marco Tulio Ribeiro and Carlos Guestrin. 2016.“why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1135–1144.
[19]
Arantxa Casanova Adriana Romero Pietro Lio Petar Velickovi, Guillem Cucurull and Yoshua Bengio. 2018. Graph Attention Networks. In Proceedings of International Conference on Learning Representations.
[20]
Tong Xu, Hao Liu, Xinjiang Lu, Shuangli Li, Jingbo Zhou and Hui Xiong. 2020. Competitive analysis for points of interest. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery Data Mining. 1265–1274.
[21]
Pu Li, Suzhi Zhang, Zijian Bai and Yuanyuan Chang. 2022. Multi-Graph Convolutional Network for Fine-Grained and Personalized POI Recommendation. Electronics 11, 18, Article 2966 (2022).
[22]
Ago Luberg, Tanel Tammet and Priit Jarv. 2013. Sightsmap: crowd-sourced popularity of the world places. In Information and Communication Technologies in Tour. Springer, 314–325.
[23]
Xueting Wang, Tetsu Kasanishi and Toshihiko Yamasaki. 2021. Edge-level explanations for graph neural networks by extending explainability methods for convolutional neural networks. In IEEE Trans. International Symposium on Multimedia. 249–252.
[24]
Greg Corrado, Tomas Mikolov, Kai Chen and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In Proceedings of International Conference on Learning Representations.
[25]
Felix T. Mavondo, Vengesayi, Sebastian and Yvette Reisinger. 2009. Tourism destination attractiveness: Attractions, facilities, and people as predictors. Tourism Analysis 14, 5 (2009), 621–636.
[26]
Vapnik Vladimir. 1999. The nature of statistical learning theory. Springer Science Business Media (1999).
[27]
Zhitao Ying, Will Hamilton and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of Advances in Neural Information Processing Systems 30. 1024–1034.
[28]
WTTC. 2023. Economic Impact Report. Global infographic (2023).
[29]
Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, Xiao Wang, Houye Ji and Philip S Yu. 2019. Heterogeneous graph attention network. In Proceedings of ACM WWW The World Wide Web Conference. 2022–2032.
[30]
Ziqiao Meng, Xinyu Fu, Jiani Zhang and Irwin King. 2020. Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In Proceedings of ACM WWW The World Wide Web Conference. 2331–2341.
[31]
Yong Liu, Shuo Shang, Bin Yao, Xuan Rao, Lisi Chen and Peng Han. 2022. GraphFlashback Network for Next Location Recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery Data Mining. 1463–1471.
[32]
Xinze Wang, Zi Huang, Ning Xie, Yang Yang, Yaqian Duan and Heng Tao Shen. 2018. Hierarchical multi-clue modelling for poi popularity prediction with heterogeneous tourist information. IEEE Transactions on Knowledge and Data Engineering 31, 4 (2018), 757–768.
[33]
Nitesh V Chawla, Yuxiao Dong and Ananthram Swami. 2017. Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 135–144.
[34]
Kuansan Wang, Ziniu Hu, Yuxiao Dong and Yizhou Sun. 2020. Heterogeneous graph transformer. In Proceedings of ACM WWW The World Wide Web Conference. 2704–2710.
[35]
Jones, K. Sparck. 1972. A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(1), 11–21.
[36]
Grootendorst, Maarten. 2022. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.
Recommendations
Heterogeneous graph knowledge distillation neural network incorporating multiple relations and cross-semantic interactions
AbstractIn recent years, the study of real-world graphs has revealed their inherent heterogeneity, prompting growing research interest in heterogeneous graphs. Characterized by diverse node and relation types, heterogeneous graphs have led to the ...
Heterogeneous graph neural networks with denoising for graph embeddings
AbstractWith the increasing popularity of graph structures, Graph embedding, Which aims to project nodes into low dimensional space while preserving the topological structure information of graphs and the information of nodes themselves, Has ...
HRMNN: Heterogeneous Relationship Mined Graph Neural Network
Advanced Intelligent Computing Technology and ApplicationsAbstractHeterogeneous graph embedding has attracted considerable attention in the field of graph data analytics. It can effectively capture relations among different types of nodes. However, existing methods have confronted with several challenges. On the ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
March 2024
229 pages
ISBN:9798400716829
DOI:10.1145/3655755
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 June 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
IVSP 2024
IVSP 2024: 2024 6th International Conference on Image, Video and Signal Processing
March 14 - 16, 2024
Ikuta, Japan
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 58Total Downloads
- Downloads (Last 12 months)58
- Downloads (Last 6 weeks)5
Reflects downloads up to 14 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format